can any 1 tell me from wer i can get the seminar report for swarm robotics
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swarm robots
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can any 1 tell me from wer i can get the seminar report for swarm robotics
16-10-2010, 02:05 PM
[attachment=6271]
Swarm Robotics
Sumit Chincholkar
Introduction
Swarm Robotics studies a particular class of multi-robot system.
It emphasis aspects like decentralization of control, robustness, flexibility and scalability.
Inspiration
Inspired by the behavior of social insects.
Each single ant acts autonomously following simple rules and locally interacting with the other ants.
A coherent and self-organizing behavior can be observed at the colony level.
The Swarm-Bot
An s-bot is a small mobile autonomous robot with self-assembling capabilities.
It weighs 700 g and its main body has a diameter of about 12 cm.
The traction system is composed of both tracks and wheels.
Cylindrical turret mounted on the chassis by means of a motorized joint.
The gripper is mounted on Turret as some freedom for lifting object.
Gripper used for connecting to S-Bots and objects.
Four proximity sensors placed under the chassis (ground sensors), torque and traction sensor.
Eight light sensors uniformly distributed around the turret ,two humidity sensor & 3-axis accelometer.
Each robot is also equipped with sensors and devices to detect and communicate with each other.
Consists of camera, LED, microphone, loudspeaker.
Synchronization
The task requires that each s-bot in the group displays a simple periodic behavior.
It means their oscillations are in phase with each other.
In order to communicate with each other, s-bots produce a continuous tone with fixed frequency and intensity.
It is perceived by every robot in arena.
The tone is perceived in a binary way.
One sensory neuron is used for perception of sound.
Co-ordinated Motion
It is a basic ability due to physical interaction among s-bots.
They must co-ordinate their action to choose common direction of movement.
It can efficiently move only if the chassis of the assembled s-bots have the same orientation.
A common direction of motion on the basis of the information provided by their traction sensor.
Four sensory neurons encode the intensity of traction along four directions, corresponding to the direction of the semi-axes of the chassis’ frame.
2 motor neurons control wheels and turret chassis motor.
Hole avoidance
Individual s-bots cannot avoid holes due to their limited perceptual apparatus.
To safely navigate arena consisting of holes
Co-ordination motion must be performed.
Presence of holes must be communicated.
Communication among the s-bots
Direct Interactions setup (DI)
Direct Communication setup (DC)
Evolved Communication setup (EC)
In all three setups (DI, DC and EC), s-bots are equipped with traction and ground sensors. In DC and EC, microphones and speakers are also used.
Advantages
Fault tolerance
De-centralized approach
Flexibility
Robustness
Limitation
Self organizing behavior.
Interaction between individual robot.
Interaction among components.
Encoding of robots.
Application
Plume tracing
Obstacle avoidance
Search and rescue
Surveillance/coverage
Convoy formation
Move towards goal by avoiding hurdles
Swarm Robotics
Sumit Chincholkar
Introduction
Swarm Robotics studies a particular class of multi-robot system.
It emphasis aspects like decentralization of control, robustness, flexibility and scalability.
Inspiration
Inspired by the behavior of social insects.
Each single ant acts autonomously following simple rules and locally interacting with the other ants.
A coherent and self-organizing behavior can be observed at the colony level.
The Swarm-Bot
An s-bot is a small mobile autonomous robot with self-assembling capabilities.
It weighs 700 g and its main body has a diameter of about 12 cm.
The traction system is composed of both tracks and wheels.
Cylindrical turret mounted on the chassis by means of a motorized joint.
The gripper is mounted on Turret as some freedom for lifting object.
Gripper used for connecting to S-Bots and objects.
Four proximity sensors placed under the chassis (ground sensors), torque and traction sensor.
Eight light sensors uniformly distributed around the turret ,two humidity sensor & 3-axis accelometer.
Each robot is also equipped with sensors and devices to detect and communicate with each other.
Consists of camera, LED, microphone, loudspeaker.
Synchronization
The task requires that each s-bot in the group displays a simple periodic behavior.
It means their oscillations are in phase with each other.
In order to communicate with each other, s-bots produce a continuous tone with fixed frequency and intensity.
It is perceived by every robot in arena.
The tone is perceived in a binary way.
One sensory neuron is used for perception of sound.
Co-ordinated Motion
It is a basic ability due to physical interaction among s-bots.
They must co-ordinate their action to choose common direction of movement.
It can efficiently move only if the chassis of the assembled s-bots have the same orientation.
A common direction of motion on the basis of the information provided by their traction sensor.
Four sensory neurons encode the intensity of traction along four directions, corresponding to the direction of the semi-axes of the chassis’ frame.
2 motor neurons control wheels and turret chassis motor.
Hole avoidance
Individual s-bots cannot avoid holes due to their limited perceptual apparatus.
To safely navigate arena consisting of holes
Co-ordination motion must be performed.
Presence of holes must be communicated.
Communication among the s-bots
Direct Interactions setup (DI)
Direct Communication setup (DC)
Evolved Communication setup (EC)
In all three setups (DI, DC and EC), s-bots are equipped with traction and ground sensors. In DC and EC, microphones and speakers are also used.
Advantages
Fault tolerance
De-centralized approach
Flexibility
Robustness
Limitation
Self organizing behavior.
Interaction between individual robot.
Interaction among components.
Encoding of robots.
Application
Plume tracing
Obstacle avoidance
Search and rescue
Surveillance/coverage
Convoy formation
Move towards goal by avoiding hurdles
02-03-2011, 09:39 AM
[attachment=9317]
Abstract
Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability(fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, space crafts, inspection/maintenance, construction, agriculture and medicine work.
Swarm-bots are a collection of mobile robots able to self assemble and to self organise in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
INTRODUCTION
Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment. It is a novel approach to the coordination of large numbers of robots. It is inspired from the observation of social insects ---ants, termites, wasps and bees--- which stand as fascinating examples of how a large number of simple individuals can interact to create collectively intelligent systems.
Social insects are known to coordinate their actions to accomplish tasks that are beyond the capabilities of a single individual: termites build large and complex mounds, army ants organize impressive foraging raids, ants can collectively carry large preys. Such coordination capabilities are still beyond the reach of current multi-robot systems.
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
1. Evolution of swarm (Biological Basis and Artificial Life)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
3.WORKING OF SWARM :
3.1Swarm Intelligence:
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
3.2.Software from insects
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
3.3.Particle swarm Optimization:
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
4.TYPES OF SWARM:
4.1.Modular Robots:
A module is essentially a small, relatively simple robot or piece of a robot. Modular robots are made of lots of these small, identical modules. A modular robot can consist of a few modules or many, depending on the robot’s design and the task it needs to perform. Some modular robots currently exist only as computer simulations; others are still in the early stages of development. But they all operate on the same basic principle- lots of little robots can combine to create one big one.
Modules can’t do much by themselves. A reconfiguring system also has to have:
Connections between the modules
Systems that govern how the modules move in relation to one another.
Most modular, reconfiguring robots fit into one of the three categories: chain, lattice and modular configuration.
4.2. Chain robots :
Chain robots are long chains that can connect to one another at specific points. Depending on the number of chains and where they connect, these robots can resemble snakes or spiders. They can also become rolling loops or bipedal, walking robots. A set of modular chains could navigate an obstacle course by crawling through a tunnel as a snake, crossing rocky terrain as a spider and riding a tricycle across a bridge as a biped. Examples of chain robots are Palo Alto Research Center’s (PARC) Polybot and Polypod and NASA’s snakebot. Most need a human or, in theory, another robot, to manually secure the connections with screws.
Abstract
Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability(fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, space crafts, inspection/maintenance, construction, agriculture and medicine work.
Swarm-bots are a collection of mobile robots able to self assemble and to self organise in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
INTRODUCTION
Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment. It is a novel approach to the coordination of large numbers of robots. It is inspired from the observation of social insects ---ants, termites, wasps and bees--- which stand as fascinating examples of how a large number of simple individuals can interact to create collectively intelligent systems.
Social insects are known to coordinate their actions to accomplish tasks that are beyond the capabilities of a single individual: termites build large and complex mounds, army ants organize impressive foraging raids, ants can collectively carry large preys. Such coordination capabilities are still beyond the reach of current multi-robot systems.
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
1. Evolution of swarm (Biological Basis and Artificial Life)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
3.WORKING OF SWARM :
3.1Swarm Intelligence:
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
3.2.Software from insects
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
3.3.Particle swarm Optimization:
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
4.TYPES OF SWARM:
4.1.Modular Robots:
A module is essentially a small, relatively simple robot or piece of a robot. Modular robots are made of lots of these small, identical modules. A modular robot can consist of a few modules or many, depending on the robot’s design and the task it needs to perform. Some modular robots currently exist only as computer simulations; others are still in the early stages of development. But they all operate on the same basic principle- lots of little robots can combine to create one big one.
Modules can’t do much by themselves. A reconfiguring system also has to have:
Connections between the modules
Systems that govern how the modules move in relation to one another.
Most modular, reconfiguring robots fit into one of the three categories: chain, lattice and modular configuration.
4.2. Chain robots :
Chain robots are long chains that can connect to one another at specific points. Depending on the number of chains and where they connect, these robots can resemble snakes or spiders. They can also become rolling loops or bipedal, walking robots. A set of modular chains could navigate an obstacle course by crawling through a tunnel as a snake, crossing rocky terrain as a spider and riding a tricycle across a bridge as a biped. Examples of chain robots are Palo Alto Research Center’s (PARC) Polybot and Polypod and NASA’s snakebot. Most need a human or, in theory, another robot, to manually secure the connections with screws.
16-03-2011, 11:47 AM
PRESENTED BY:
E.UDAY KIRAN
[attachment=10292]
Swarm robots
WHAT IS A “SWARM ROBOTs”?
Swarm-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot.
A boid must observe the following rules :
• Avoidance rule
• Copy rule
• The center rule
• View
SWARM INTELLIGENCE
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules.
SOFTWARE FROM INSECTS :
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm.
Ants , bees and termites are beautifully engineered examples of this kind of software in use.
developing swarm software from “group behavior building blocks” that can be combined to form larger, more complex applications are being developed.
ANT COLONY OPTIMIZATION
Ant colony optimization or ACO is a meta heuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems.
ACO has been successfully applied to an impressive number of optimization problems.
APPLICATION OF ROBOT SWARMS:
There are many applications for swarms of robots.
Multiple vacuum cleaner robots .
Robots used for earthquake rescue .
A swarm of mars rovers.
OTHER TYPES OF SWARM ROBOTS :
Modular Robots : Lots of little robots can combine to create one big one.
Chain robots :
1. Long chains that can connect to one another at specific points .
2. Depending on the number of chains and where they connect, these robots can resemble snakes or spiders.
EXAMPLES:
a. Palo Alto Research Center’s (PARC) Polybot
b. Polypod
c. NASA’s snakebot
Lattice robot : Lattice robots move by crawling over one another, attaching to and detaching from connection points on neighboring robots.
• Modules can either have self-contained power sources, or they can share power sources through their connections to other modules.
Mobile reconfiguration robots are small,identical modules that can combine to form bigger robots.
(1)However, they don’t need their neighbors’
help to get from place to place- they can
move around on their own.
(2) They move independently until they
need to come together to accomplish
a specific task.
A GLANCE AT THE OTHER APPLICATIONS :
• Transport by groups of blind and non-blind robots.
Transport of objects of different shapes and sizes
CONCLUSION
1.Robots are going to be an important part of the future.
2.Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind.
3.Software designed to run on large groups of robots is the key needed to unlock this potential.
Group selection
Consider a single gene locus with the alleles , which have the fitnesses and the allele frequencies respectively. Ignoring frequency-dependent selection, then genetic load (L) may be calculated as:
where wmax is the maximum value of the fitnesses and is mean fitness which is calculated as the mean of all the fitnesses weighted by their corresponding allele frequency:
If the mean fitness is 0, the load is equal to 1, but the population goes extinct.
where the ith allele is and has the fitness and frequency wi and pi respectively.
When the wmax = 1, then (1) simplifies to
Causes of genetic load
Load may be caused by selection and mutation.
Mutational load
This section requires expansion.Mutation load is caused when a mutation at a locus produces a new allele of either lesser or greater fitness. This lowers the average fitness of the population; a deleterious mutation has a lower relative fitness, lowering average load, while an advantageous mutation effectively increases the relative fitness of the existing allele, and thus also increases average fitness.
Selectional load
This section requires expansion.Selection occurs when the fitnesses of particular alleles are inequal, hence selection always exerts a load.
With directional selection, the allele frequencies will tend towards an equilibrium position with the fittest allele reaching a frequency in mutation-selection balance. As mutations are rare, this is effectively fixation. Consider two alleles and . If w1 > w2, then at equilibrium, and , hence , and .
Group selection
Consider a single gene locus with the alleles , which have the fitnesses and the allele frequencies respectively. Ignoring frequency-dependent selection, then genetic load (L) may be calculated as:
where wmax is the maximum value of the fitnesses and is mean fitness which is calculated as the mean of all the fitnesses weighted by their corresponding allele frequency:
If the mean fitness is 0, the load is equal to 1, but the population goes extinct.
where the ith allele is and has the fitness and frequency wi and pi respectively.
When the wmax = 1, then (1) simplifies to
Causes of genetic load
Load may be caused by selection and mutation.
Mutational load
This section requires expansion.Mutation load is caused when a mutation at a locus produces a new allele of either lesser or greater fitness. This lowers the average fitness of the population; a deleterious mutation has a lower relative fitness, lowering average load, while an advantageous mutation effectively increases the relative fitness of the existing allele, and thus also increases average fitness.
Selectional load
This section requires expansion.Selection occurs when the fitnesses of particular alleles are inequal, hence selection always exerts a load.
With directional selection, the allele frequencies will tend towards an equilibrium position with the fittest allele reaching a frequency in mutation-selection balance. As mutations are rare, this is effectively fixation. Consider two alleles and . If w1 > w2, then at equilibrium, and , hence , and .
Directional selection is a particular mode or mechanism of natural selection. In population genetics, directional selection occurs when natural selection favors a singlephenotype and therefore allele frequency continuously shifts in one direction. Under directional selection, the advantageous allele will increase in frequency independently of its dominance relative to other alleles (i.e. even if the advantageous allele is recessive, it will eventually become fixed). Directional selection stands in contrast tobalancing selection where selection may favor multiple alleles, and is the same as purifying selection which removes deleterious mutations from a population.
E.UDAY KIRAN
[attachment=10292]
Swarm robots
WHAT IS A “SWARM ROBOTs”?
Swarm-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot.
A boid must observe the following rules :
• Avoidance rule
• Copy rule
• The center rule
• View
SWARM INTELLIGENCE
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules.
SOFTWARE FROM INSECTS :
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm.
Ants , bees and termites are beautifully engineered examples of this kind of software in use.
developing swarm software from “group behavior building blocks” that can be combined to form larger, more complex applications are being developed.
ANT COLONY OPTIMIZATION
Ant colony optimization or ACO is a meta heuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems.
ACO has been successfully applied to an impressive number of optimization problems.
APPLICATION OF ROBOT SWARMS:
There are many applications for swarms of robots.
Multiple vacuum cleaner robots .
Robots used for earthquake rescue .
A swarm of mars rovers.
OTHER TYPES OF SWARM ROBOTS :
Modular Robots : Lots of little robots can combine to create one big one.
Chain robots :
1. Long chains that can connect to one another at specific points .
2. Depending on the number of chains and where they connect, these robots can resemble snakes or spiders.
EXAMPLES:
a. Palo Alto Research Center’s (PARC) Polybot
b. Polypod
c. NASA’s snakebot
Lattice robot : Lattice robots move by crawling over one another, attaching to and detaching from connection points on neighboring robots.
• Modules can either have self-contained power sources, or they can share power sources through their connections to other modules.
Mobile reconfiguration robots are small,identical modules that can combine to form bigger robots.
(1)However, they don’t need their neighbors’
help to get from place to place- they can
move around on their own.
(2) They move independently until they
need to come together to accomplish
a specific task.
A GLANCE AT THE OTHER APPLICATIONS :
• Transport by groups of blind and non-blind robots.
Transport of objects of different shapes and sizes
CONCLUSION
1.Robots are going to be an important part of the future.
2.Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind.
3.Software designed to run on large groups of robots is the key needed to unlock this potential.
Group selection
Consider a single gene locus with the alleles , which have the fitnesses and the allele frequencies respectively. Ignoring frequency-dependent selection, then genetic load (L) may be calculated as:
where wmax is the maximum value of the fitnesses and is mean fitness which is calculated as the mean of all the fitnesses weighted by their corresponding allele frequency:
If the mean fitness is 0, the load is equal to 1, but the population goes extinct.
where the ith allele is and has the fitness and frequency wi and pi respectively.
When the wmax = 1, then (1) simplifies to
Causes of genetic load
Load may be caused by selection and mutation.
Mutational load
This section requires expansion.Mutation load is caused when a mutation at a locus produces a new allele of either lesser or greater fitness. This lowers the average fitness of the population; a deleterious mutation has a lower relative fitness, lowering average load, while an advantageous mutation effectively increases the relative fitness of the existing allele, and thus also increases average fitness.
Selectional load
This section requires expansion.Selection occurs when the fitnesses of particular alleles are inequal, hence selection always exerts a load.
With directional selection, the allele frequencies will tend towards an equilibrium position with the fittest allele reaching a frequency in mutation-selection balance. As mutations are rare, this is effectively fixation. Consider two alleles and . If w1 > w2, then at equilibrium, and , hence , and .
Group selection
Consider a single gene locus with the alleles , which have the fitnesses and the allele frequencies respectively. Ignoring frequency-dependent selection, then genetic load (L) may be calculated as:
where wmax is the maximum value of the fitnesses and is mean fitness which is calculated as the mean of all the fitnesses weighted by their corresponding allele frequency:
If the mean fitness is 0, the load is equal to 1, but the population goes extinct.
where the ith allele is and has the fitness and frequency wi and pi respectively.
When the wmax = 1, then (1) simplifies to
Causes of genetic load
Load may be caused by selection and mutation.
Mutational load
This section requires expansion.Mutation load is caused when a mutation at a locus produces a new allele of either lesser or greater fitness. This lowers the average fitness of the population; a deleterious mutation has a lower relative fitness, lowering average load, while an advantageous mutation effectively increases the relative fitness of the existing allele, and thus also increases average fitness.
Selectional load
This section requires expansion.Selection occurs when the fitnesses of particular alleles are inequal, hence selection always exerts a load.
With directional selection, the allele frequencies will tend towards an equilibrium position with the fittest allele reaching a frequency in mutation-selection balance. As mutations are rare, this is effectively fixation. Consider two alleles and . If w1 > w2, then at equilibrium, and , hence , and .
Directional selection is a particular mode or mechanism of natural selection. In population genetics, directional selection occurs when natural selection favors a singlephenotype and therefore allele frequency continuously shifts in one direction. Under directional selection, the advantageous allele will increase in frequency independently of its dominance relative to other alleles (i.e. even if the advantageous allele is recessive, it will eventually become fixed). Directional selection stands in contrast tobalancing selection where selection may favor multiple alleles, and is the same as purifying selection which removes deleterious mutations from a population.
09-04-2011, 04:28 PM
Presented By:
GOUSE ADHAM K
[attachment=11954]
ABSTRACT:
This paper presents an introduction to the world of swarm robots and adumbrates its applications .Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, spacecraft, Inspection/maintenance, construction, agriculture, and medicine work .Swarm-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment .Robots are going to be an important part of the future. In the near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees. Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind. Software designed to run on large groups of robots is the key needed to unlock this potential.
WHAT IS A “SWARM”?
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
EVOLUTION OF SWARM (BIOLOGICAL BASIS AND ARTIFICIAL LIFE)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, and then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
SWARM INTELLIGENCE
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.
Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
SOFTWARE FROM INSECTS
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
ANT COLONY OPTIMIZATION
Ant colony optimization or ACO is a meta heuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants,
deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems.
PARTICLE SWARM OPTIMIZATION
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
APPLICATION OF ROBOT SWARMS
There are many applications for swarms of robots. Multiple vacuum cleaner robots might need to share maps of areas where they have previously cleaned. A swarm of mars rovers might need to disperse throughout the environment to locate promising areas, while maintaining communications with each other. Robots used for earthquake rescue might come in three flavors: thousands for cockroach sized scouts to infiltrate the debris and locate survivors, a few dozen rat-sized structural engineers to get near the scene and solve the “pick-up-sticks” problem of getting the rubble off, and a few brontosaurus-sized heavy lifters to carry out the rescue plan.
In all these applications, individual robots must work independently, only communicating with other nearby robots. It is either too expensive (robot vacuums need to be very cheap, too far it takes 15 minutes for messages to get to Mars), or impossible (radio control signals cannot
penetrate into earthquake rubble) to control all of the robots from a centralized location. However, a distributed control system can let robots from a centralized location.
However, a distributed control system can let robots interact with other nearby robots, cooperating amongst themselves to accomplish their mission.
Journey into small spaces:
The mini-machines could travel in swarms like insects and go into locations too small for their bulkier cousins, communicating all the while with each other and human operators in a remote location.
Eventually fleets of robots could scamper through pipes looking for chemical releases of patrol buildings in search of prowlers. Taking the smaller robots in large numbers have the better chances of finding what we are looking for.
Currently these robots can navigate a field of coins, puttering along at 20 inches (50 cm) a minute on track wheels similar to those on tanks. The treads give added mobility over predecessors with conventional wheels, allowing it to travel over thick carpet. Though they can’t zip along as fast as a spider or ant yet, with modifications it could go up to five times faster.
GOUSE ADHAM K
[attachment=11954]
ABSTRACT:
This paper presents an introduction to the world of swarm robots and adumbrates its applications .Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, spacecraft, Inspection/maintenance, construction, agriculture, and medicine work .Swarm-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment .Robots are going to be an important part of the future. In the near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees. Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind. Software designed to run on large groups of robots is the key needed to unlock this potential.
WHAT IS A “SWARM”?
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
EVOLUTION OF SWARM (BIOLOGICAL BASIS AND ARTIFICIAL LIFE)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, and then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
SWARM INTELLIGENCE
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.
Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
SOFTWARE FROM INSECTS
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
ANT COLONY OPTIMIZATION
Ant colony optimization or ACO is a meta heuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants,
deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems.
PARTICLE SWARM OPTIMIZATION
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
APPLICATION OF ROBOT SWARMS
There are many applications for swarms of robots. Multiple vacuum cleaner robots might need to share maps of areas where they have previously cleaned. A swarm of mars rovers might need to disperse throughout the environment to locate promising areas, while maintaining communications with each other. Robots used for earthquake rescue might come in three flavors: thousands for cockroach sized scouts to infiltrate the debris and locate survivors, a few dozen rat-sized structural engineers to get near the scene and solve the “pick-up-sticks” problem of getting the rubble off, and a few brontosaurus-sized heavy lifters to carry out the rescue plan.
In all these applications, individual robots must work independently, only communicating with other nearby robots. It is either too expensive (robot vacuums need to be very cheap, too far it takes 15 minutes for messages to get to Mars), or impossible (radio control signals cannot
penetrate into earthquake rubble) to control all of the robots from a centralized location. However, a distributed control system can let robots from a centralized location.
However, a distributed control system can let robots interact with other nearby robots, cooperating amongst themselves to accomplish their mission.
Journey into small spaces:
The mini-machines could travel in swarms like insects and go into locations too small for their bulkier cousins, communicating all the while with each other and human operators in a remote location.
Eventually fleets of robots could scamper through pipes looking for chemical releases of patrol buildings in search of prowlers. Taking the smaller robots in large numbers have the better chances of finding what we are looking for.
Currently these robots can navigate a field of coins, puttering along at 20 inches (50 cm) a minute on track wheels similar to those on tanks. The treads give added mobility over predecessors with conventional wheels, allowing it to travel over thick carpet. Though they can’t zip along as fast as a spider or ant yet, with modifications it could go up to five times faster.
11-04-2011, 11:31 AM
Presented By:
GOUSE ADHAM K
[attachment=11993]
ABSTRACT:
This paper presents an introduction to the world of swarm robots and adumbrates its applications .Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, spacecraft, Inspection/maintenance, construction, agriculture, and medicine work .Swarm-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment .Robots are going to be an important part of the future. In the near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees. Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind. Software designed to run on large groups of robots is the key needed to unlock this potential.
WHAT IS A “SWARM”?
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
EVOLUTION OF SWARM (BIOLOGICAL BASIS AND ARTIFICIAL LIFE)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, and then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
SWARM INTELLIGENCE
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.
Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
SOFTWARE FROM INSECTS
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
ANT COLONY OPTIMIZATION
Ant colony optimization or ACO is a meta heuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants,
deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems.
PARTICLE SWARM OPTIMIZATION
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
APPLICATION OF ROBOT SWARMS
There are many applications for swarms of robots. Multiple vacuum cleaner robots might need to share maps of areas where they have previously cleaned. A swarm of mars rovers might need to disperse throughout the environment to locate promising areas, while maintaining communications with each other. Robots used for earthquake rescue might come in three flavors: thousands for cockroach sized scouts to infiltrate the debris and locate survivors, a few dozen rat-sized structural engineers to get near the scene and solve the “pick-up-sticks” problem of getting the rubble off, and a few brontosaurus-sized heavy lifters to carry out the rescue plan.
In all these applications, individual robots must work independently, only communicating with other nearby robots. It is either too expensive (robot vacuums need to be very cheap, too far it takes 15 minutes for messages to get to Mars), or impossible (radio control signals cannot
penetrate into earthquake rubble) to control all of the robots from a centralized location. However, a distributed control system can let robots from a centralized location.
However, a distributed control system can let robots interact with other nearby robots, cooperating amongst themselves to accomplish their mission.
Journey into small spaces:
The mini-machines could travel in swarms like insects and go into locations too small for their bulkier cousins, communicating all the while with each other and human operators in a remote location.
Eventually fleets of robots could scamper through pipes looking for chemical releases of patrol buildings in search of prowlers. Taking the smaller robots in large numbers have the better chances of finding what we are looking for.
Currently these robots can navigate a field of coins, puttering along at 20 inches (50 cm) a minute on track wheels similar to those on tanks. The treads give added mobility over predecessors with conventional wheels, allowing it to travel over thick carpet. Though they can’t zip along as fast as a spider or ant yet, with modifications it could go up to five times faster.
Covert uses possible:
The size of the robot is limited by the size of its power source. The frame must be large enough to hold three watch batteries, which drive its motors and instruments. The robot could play a major role in intelligence gathering. Over the next several years these mini robot can be fitted with impressive options, including video cameras and infrared or radio wireless two-way communications.
Terminators, Transformers and Other Self-Reconfiguring Robots:
The coolest thing about Transformers, of course, is that they can take two completely different shapes. Most can be bipedal robots or working vehicles. Some can instead transform into weapons or electronic devices. A Transformer’s two forms have vast different strengths and capabilities.
This is completely different from most real robots, which are usually only good at performing one task or a few related tasks. The Mars Exploration Rovers, for example, can do the following:
Generate power with solar calls and store it in batteries
Drive across the landscape
Take pictures
Drill into rocks
Use spectrometers to record temperatures, chemical compositions, X-rays and alpha particles
Send the recorded data back to Earth using radio waves
GOUSE ADHAM K
[attachment=11993]
ABSTRACT:
This paper presents an introduction to the world of swarm robots and adumbrates its applications .Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, spacecraft, Inspection/maintenance, construction, agriculture, and medicine work .Swarm-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment .Robots are going to be an important part of the future. In the near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees. Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind. Software designed to run on large groups of robots is the key needed to unlock this potential.
WHAT IS A “SWARM”?
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
EVOLUTION OF SWARM (BIOLOGICAL BASIS AND ARTIFICIAL LIFE)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, and then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
SWARM INTELLIGENCE
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.
Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
SOFTWARE FROM INSECTS
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
ANT COLONY OPTIMIZATION
Ant colony optimization or ACO is a meta heuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants,
deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems.
PARTICLE SWARM OPTIMIZATION
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
APPLICATION OF ROBOT SWARMS
There are many applications for swarms of robots. Multiple vacuum cleaner robots might need to share maps of areas where they have previously cleaned. A swarm of mars rovers might need to disperse throughout the environment to locate promising areas, while maintaining communications with each other. Robots used for earthquake rescue might come in three flavors: thousands for cockroach sized scouts to infiltrate the debris and locate survivors, a few dozen rat-sized structural engineers to get near the scene and solve the “pick-up-sticks” problem of getting the rubble off, and a few brontosaurus-sized heavy lifters to carry out the rescue plan.
In all these applications, individual robots must work independently, only communicating with other nearby robots. It is either too expensive (robot vacuums need to be very cheap, too far it takes 15 minutes for messages to get to Mars), or impossible (radio control signals cannot
penetrate into earthquake rubble) to control all of the robots from a centralized location. However, a distributed control system can let robots from a centralized location.
However, a distributed control system can let robots interact with other nearby robots, cooperating amongst themselves to accomplish their mission.
Journey into small spaces:
The mini-machines could travel in swarms like insects and go into locations too small for their bulkier cousins, communicating all the while with each other and human operators in a remote location.
Eventually fleets of robots could scamper through pipes looking for chemical releases of patrol buildings in search of prowlers. Taking the smaller robots in large numbers have the better chances of finding what we are looking for.
Currently these robots can navigate a field of coins, puttering along at 20 inches (50 cm) a minute on track wheels similar to those on tanks. The treads give added mobility over predecessors with conventional wheels, allowing it to travel over thick carpet. Though they can’t zip along as fast as a spider or ant yet, with modifications it could go up to five times faster.
Covert uses possible:
The size of the robot is limited by the size of its power source. The frame must be large enough to hold three watch batteries, which drive its motors and instruments. The robot could play a major role in intelligence gathering. Over the next several years these mini robot can be fitted with impressive options, including video cameras and infrared or radio wireless two-way communications.
Terminators, Transformers and Other Self-Reconfiguring Robots:
The coolest thing about Transformers, of course, is that they can take two completely different shapes. Most can be bipedal robots or working vehicles. Some can instead transform into weapons or electronic devices. A Transformer’s two forms have vast different strengths and capabilities.
This is completely different from most real robots, which are usually only good at performing one task or a few related tasks. The Mars Exploration Rovers, for example, can do the following:
Generate power with solar calls and store it in batteries
Drive across the landscape
Take pictures
Drill into rocks
Use spectrometers to record temperatures, chemical compositions, X-rays and alpha particles
Send the recorded data back to Earth using radio waves
11-04-2011, 12:46 PM
[attachment=12005]
1.INTRODUCTION
Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment. It is a novel approach to the coordination of large numbers of robots. It is inspired from the observation of social insects ---ants, termites, wasps and bees--- which stand as fascinating examples of how a large number of simple individuals can interact to create collectively intelligent systems.
Social insects are known to coordinate their actions to accomplish tasks that are beyond the capabilities of a single individual: termites build large and complex mounds, army ants organize impressive foraging raids, ants can collectively carry large preys. Such coordination capabilities are still beyond the reach of current multi-robot systems.
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
2.EVOLUTION OF SWARM
Biological basis and artificial life)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
3.WORKING OF SWARM :
3.1Swarm Intelligence:
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
3.2. Software from insects:
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
3.3. Particle swarm Optimization:
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
4.TYPES OF SWARM:
4.1.Modular Robots:
A module is essentially a small, relatively simple robot or piece of a robot. Modular robots are made of lots of these small, identical modules. A modular robot can consist of a few modules or many, depending on the robot’s design and the task it needs to perform. Some modular robots currently exist only as computer simulations; others are still in the early stages of development. But they all operate on the same basic principle- lots of little robots can combine to create one big one.
Modules can’t do much by themselves. A reconfiguring system also has to have:
Connections between the modules
Systems that govern how the modules move in relation to one another.
Most modular, reconfiguring robots fit into one of the three categories: chain, lattice and modular configuration.
4.2. Chain robots :
Chain robots are long chains that can connect to one another at specific points. Depending on the number of chains and where they connect, these robots can resemble snakes or spiders. They can also become rolling loops or bipedal, walking robots. A set of modular chains could navigate an obstacle course by crawling through a tunnel as a snake, crossing rocky terrain as a spider and riding a tricycle across a bridge as a biped. Examples of chain robots are Palo Alto Research Center’s (PARC) Polybot and Polypod and NASA’s snakebot. Most need a human or, in theory, another robot, to manually secure the connections with screws.
1.INTRODUCTION
Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment. It is a novel approach to the coordination of large numbers of robots. It is inspired from the observation of social insects ---ants, termites, wasps and bees--- which stand as fascinating examples of how a large number of simple individuals can interact to create collectively intelligent systems.
Social insects are known to coordinate their actions to accomplish tasks that are beyond the capabilities of a single individual: termites build large and complex mounds, army ants organize impressive foraging raids, ants can collectively carry large preys. Such coordination capabilities are still beyond the reach of current multi-robot systems.
As robots become more and more useful, multiple robots working together on a single task will become common place. Many of the most useful applications of robots are particularly well suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally difficult to program and co-ordinate.
Swarm robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.
2.EVOLUTION OF SWARM
Biological basis and artificial life)Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids’ velocities and directions. Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
3.WORKING OF SWARM :
3.1Swarm Intelligence:
Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules. For example, ants use the approach to find the most efficient route to the food source.Individual ants do nothing more than follow the strongest pheromone trail left by other ants. But, by repeated process of trial and error by many ants, the best route to the food is quickly revealed.
3.2. Software from insects:
Local interactions between nearby robots are being used to produce large scale group behaviors from the entire swarm. Ants , bees and termites are beautifully engineered examples of this kind of software in use. These insects do not use centralized communication; there is no strict hierarchy, and no one in charge.
However, developing swarm software from the “top down”, i.e., by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed. The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting. Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation. There are currently about forty of these behaviors. They are designed to produce predictable outcomes when used individually, are when combined with other library behaviors, allowing group applications to be constructed much more easily.
3.3. Particle swarm Optimization:
Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large numbers of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.
4.TYPES OF SWARM:
4.1.Modular Robots:
A module is essentially a small, relatively simple robot or piece of a robot. Modular robots are made of lots of these small, identical modules. A modular robot can consist of a few modules or many, depending on the robot’s design and the task it needs to perform. Some modular robots currently exist only as computer simulations; others are still in the early stages of development. But they all operate on the same basic principle- lots of little robots can combine to create one big one.
Modules can’t do much by themselves. A reconfiguring system also has to have:
Connections between the modules
Systems that govern how the modules move in relation to one another.
Most modular, reconfiguring robots fit into one of the three categories: chain, lattice and modular configuration.
4.2. Chain robots :
Chain robots are long chains that can connect to one another at specific points. Depending on the number of chains and where they connect, these robots can resemble snakes or spiders. They can also become rolling loops or bipedal, walking robots. A set of modular chains could navigate an obstacle course by crawling through a tunnel as a snake, crossing rocky terrain as a spider and riding a tricycle across a bridge as a biped. Examples of chain robots are Palo Alto Research Center’s (PARC) Polybot and Polypod and NASA’s snakebot. Most need a human or, in theory, another robot, to manually secure the connections with screws.
11-04-2011, 03:19 PM
PRESENTED BY
B.n v manikanta
[attachment=12022]
SWARM ROBOTICS: A DIFFERENT APPROACH TO SERVICE ROBOTICS
OBJECTIVE
Robots are going to be an important part of the future.
Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind.
Software designed to run on large groups of robots is the key needed to unlock this potential
INTRODUCTION
SWARM ROBOTICS IS . . . . .
inspired from the observation of Social insects
It is a novel approach to the coordination of large number of robots
EVOLUTION OF SWARM
In 1987, Reynolds created a “boid” model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment.
A boid must observe the following rules. First, the “avoidance rule” says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions.
Second, the “copy rule” says a boid must fly in the general direction that the flock is moving by averaging the other boids velocities and directions
Third, “the center rule” says that a boid should minimize exposure flock’s exterior by moving toward the perceived center of the flock.
A fourth rule, “view” that indicates that a boid should move laterally away from any boid that blocks its view.
The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.
WORKING OF SWARM
SWARM INTELLIGENCE : Swarm intelligence describes the way that complex behaviors can arise from large numbers of individual agents each following very simple rules.
SOFTWARE FROM INSCETS :
Instead a “group behavior building blocks” that can be combined to form larger, more complex applications are being developed.
The robots use these behaviors to communicate, cooperate, and move relative to each other. Some behaviors are simple, like following, dispersing, and counting.
Some are more complex, like dynamic task assignment, temporal synchronization, and gradient tree navigation
TYPES OF SWARM
MODULAR ROBOTS :
Connections between the modules
Systems that govern how the modules move in
relation to one another
CHAIN ROBOTS
Chain robots are long chains that can connect to one another at specific points.
Depending on the number of chains and where they connect, these robots can resemble snakes or spiders
NASA’S SNAKE BOAT
WATER SKATER
A bug like robot inspired by insects that skate across water has been engineered.
The machine provides deeper insight into hoe these long legged bugs known as water striders or pond skaters move.
Tiny hairs on the ends of its legs that repel water keep the actual Insect of afloat.
These machines made buoyant by dipping the legs in water resistant Teflon solution
ADVANTAGES
Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems.
They can accomplish some tasks that would be impossible for a single robot to achieve.
Swarm-bots are a collection of mobile robots able to self assemble and to self organize in order to solve problems that cannot be solved by a single robot.
These robots combine the power of swarm intelligence with the flexibility of self reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
APPLICATIONS
Terminators, Transformers and Other Self-Reconfiguring Robots
This is completely different from most real robots, which are usually only good at performing one task or a few related tasks. The Mars Exploration Rovers, for example, can do the following:
Generate power with solar calls and store it in batteries.
Drive across the landscape.
Take pictures.
Drill into rocks.
Use spectrometers to record temperatures, chemical compositions, X-rays and alpha particles
Send the recorded data back to Earth using radio waves.
CONCLUSION
Robots are going to be an important part of the future. Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind. Software designed to run on large groups of robots is the key needed to unlock this potential
11-04-2011, 04:14 PM
PRESENTED BY:
B.NARESH
SWARM ROBOTICS: A DIFFERENT APPROACH TO SERVICE ROBOTICS
ABSTRACT
Service robotics, as it has been intended so far, views the accomplishment of a service mission mainly as the result of the action of a single robot. Swarm robotics tackles the very same problem from a different instance i.e., as the result of a team effort of simple units. The paper described here shows this particular approach.
It first defines one simple unit (s-bot) capable of independently moving about on the ground and of dynamically establishing rigid or semi-rigid connections with other fellow units, and then it shows how a large group of them can, as a whole entity (swarm-bot), carry out a given task. Thanks to the ductility in assembling and forming its connections, a swarm-bot can readily cope with occasional failures of some components and promptly reshape the remaining swarm so as to replace the role of the failing units. Given such a plasticity, their possible applications are rather large ranging from harsh environment exploration to goods harvesting or goods transportation. At the moment, the project is at the stage of having defined a first simulating environment to be used both for the on-going hardware design and for the software control. The present paper describes this particular aspect of the project
B.NARESH
SWARM ROBOTICS: A DIFFERENT APPROACH TO SERVICE ROBOTICS
ABSTRACT
Service robotics, as it has been intended so far, views the accomplishment of a service mission mainly as the result of the action of a single robot. Swarm robotics tackles the very same problem from a different instance i.e., as the result of a team effort of simple units. The paper described here shows this particular approach.
It first defines one simple unit (s-bot) capable of independently moving about on the ground and of dynamically establishing rigid or semi-rigid connections with other fellow units, and then it shows how a large group of them can, as a whole entity (swarm-bot), carry out a given task. Thanks to the ductility in assembling and forming its connections, a swarm-bot can readily cope with occasional failures of some components and promptly reshape the remaining swarm so as to replace the role of the failing units. Given such a plasticity, their possible applications are rather large ranging from harsh environment exploration to goods harvesting or goods transportation. At the moment, the project is at the stage of having defined a first simulating environment to be used both for the on-going hardware design and for the software control. The present paper describes this particular aspect of the project
27-04-2011, 11:43 AM
Presented by
Shaiksha Vali. S
[attachment=12919]
SWARM ROBOTICS
Swarm robotics
Introduction to swarm robotics
Biological inspiration
Characteristics of swarm robotics
Algorithms of swarm robotics
Communication in swarm robotics
Co-ordination in swarm robotics
Reconfigurable swarm robotics
Applications
INTRODUCTION TO SWARM ROBOTICS
Robot
Swarm robot
Swarm robotics
Definition :Swarm robotics is branch of robotics which is a new approach to the coordination of multi robot system which consist of large numbers of mostly simple physical robots. It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment
Biological inspiration
Swarm intelligence
“The emergent collective intelligence of groups of simple agents.”
Foraging Behavior
Many ant species forage for food using a trail- laying trail- following behavior. As explorer ants leave their nest in search of food they leave a trail of pheromones. The foraging ants trek across the land in a random walk fashion, some of them eventually finding a food source. When an ant discovers a food source, it retraces its route back to the nest – still leaving pheromones. Hence the amount of pheromone on the trail to the food source is now twice as intense as any other trail leaving the nest. Other ants are then attracted by the pheromones and follow the trail left by the pioneering ant. These subsequent ants also leave a trail of pheromones, further intensifying the odor
Characteristics
Robustness
Flexibility
De-centralized approach
scalability
Algorithms of swarm robotics
Dispersion in indoor environment
Mobile formations
Co-operative hole avoidance
Chain based path formation
Chain based path formation
The S-bot controller for this algorithm is very simple and elegant. The controller is designed as a simple state-machine that consists of four states: search, explore, chain and finished. Each state is associated with a repetitive task that the S-bot performs until sensory data causes the robot to transition to another state.
procedure
• Search – randomly walk around the arena and avoid obstacles with infrared sensors. No LEDs
are illuminated in this state since the S-bot is not contributing to the location of the prey.
• Explore – move along a chain by following colors. A chain always follows the following pattern:
blue then green then yellow, repeated. From this information a robot knows which direction
down a chain it is going (i.e. the S-bot will see BGYBGYBG... if moving g away from the nest
and will see BYGBYGBYG... if moving towards the nest).
• Search ! Explore – if a chain member has been detected to be close.
• Chain – be a member of the chain with the color based on the previous S-bot in the chain.
Actively try to maintain a specific distance from both neighbors in the chain. Also, actively
maintain a 180 angle between both neighbors, give or take (i.e. approximately opposite
sides).
• Explore ! Chain – if the prey is close, join the chain or if the tail of a chain is reached, join
with it
• Chain ! Explore – If at the tail of a chain, leave the chain
• Finished – the task has been completed. Now it can move on to other tasks, such as object
transport.
• Explore! Finished – if the destination is really close.
• Explore ! Search – no chain member is in sight. This will only be caused by an error or fault.
Simple S-bot
An s-bot is a small mobile autonomous robot with self-assembling capabilities.
It weighs 700 g and its main body has a diameter of about 12 cm.
The traction system is composed of both tracks and wheels.
The gripper is mounted on Turret as some freedom for lifting object.
Gripper used for connecting to S-Bots and objects.
Four proximity sensors placed under the chassis (ground sensors), torque and traction sensor.
Eight light sensors uniformly distributed around the turret ,two humidity sensor & 3-axis accelerometer.
Each robot is also equipped with sensors and devices to detect and communicate with each other.
Consists of transmitter, receiver ,camera, LED, microphone, loudspeaker.
Types of communication
Any task requires communication between the robots therefore the principle is that each s-bot in the group displays a simple periodic behavior. It means their oscillations are in phase with each other. In order to communicate with each other, s-bots produce a continuous tone with fixed frequency and intensity. It is perceived by every robot in arena. The tone is perceived in a binary way.
Communication actually takes place using ad-hoc wireless communications
like RF or I
Two types of communication 1.Interaction via sensing 2.Interaction via communication
Interaction via sensing
if two robots interacting to pull a stick and sensing each other’s action in a limited way , this work is considered as “interaction via sensing”.
Interaction via sensing requires the discrimination of other robots from the environment objects, also called as the kin recognition.
kin recognition is considered as a kind of minimalist communication mechanism since just by discriminating the kin and observing their behaviors (without explicit communication),the robots can manage to solve several problems (e.g. flocking, chain formation and cooperative stick pulling) in swarm robotics.
Interaction via communication
robots broadcasts information packages directly or switches on/off a light around them to show their state, these studies are considered to be the type of “interaction via communication”
If the sender in the interaction aims to give information to other robots intentionally then that study is categorized as “interaction via communication”
Co-ordination
Centralized: the organization of a system having a robotic agent (a leader) that is in charge of organizing the work of the other robots; the leader is involved in the decisional process for the whole team, while the other members act according to the directions of the leader.
Decentralized: the organization of a system composed by robotic agents which are completely autonomous in the decisional process with respect to each other; in this class of systems a leader does not exist.
Reconfigurable swarm bots
Lattice architecture
Chain architecture
Mobile architecture
Applications
Plume tracing
Obstacle avoidance
Search and rescue
Shaiksha Vali. S
[attachment=12919]
SWARM ROBOTICS
Swarm robotics
Introduction to swarm robotics
Biological inspiration
Characteristics of swarm robotics
Algorithms of swarm robotics
Communication in swarm robotics
Co-ordination in swarm robotics
Reconfigurable swarm robotics
Applications
INTRODUCTION TO SWARM ROBOTICS
Robot
Swarm robot
Swarm robotics
Definition :Swarm robotics is branch of robotics which is a new approach to the coordination of multi robot system which consist of large numbers of mostly simple physical robots. It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment
Biological inspiration
Swarm intelligence
“The emergent collective intelligence of groups of simple agents.”
Foraging Behavior
Many ant species forage for food using a trail- laying trail- following behavior. As explorer ants leave their nest in search of food they leave a trail of pheromones. The foraging ants trek across the land in a random walk fashion, some of them eventually finding a food source. When an ant discovers a food source, it retraces its route back to the nest – still leaving pheromones. Hence the amount of pheromone on the trail to the food source is now twice as intense as any other trail leaving the nest. Other ants are then attracted by the pheromones and follow the trail left by the pioneering ant. These subsequent ants also leave a trail of pheromones, further intensifying the odor
Characteristics
Robustness
Flexibility
De-centralized approach
scalability
Algorithms of swarm robotics
Dispersion in indoor environment
Mobile formations
Co-operative hole avoidance
Chain based path formation
Chain based path formation
The S-bot controller for this algorithm is very simple and elegant. The controller is designed as a simple state-machine that consists of four states: search, explore, chain and finished. Each state is associated with a repetitive task that the S-bot performs until sensory data causes the robot to transition to another state.
procedure
• Search – randomly walk around the arena and avoid obstacles with infrared sensors. No LEDs
are illuminated in this state since the S-bot is not contributing to the location of the prey.
• Explore – move along a chain by following colors. A chain always follows the following pattern:
blue then green then yellow, repeated. From this information a robot knows which direction
down a chain it is going (i.e. the S-bot will see BGYBGYBG... if moving g away from the nest
and will see BYGBYGBYG... if moving towards the nest).
• Search ! Explore – if a chain member has been detected to be close.
• Chain – be a member of the chain with the color based on the previous S-bot in the chain.
Actively try to maintain a specific distance from both neighbors in the chain. Also, actively
maintain a 180 angle between both neighbors, give or take (i.e. approximately opposite
sides).
• Explore ! Chain – if the prey is close, join the chain or if the tail of a chain is reached, join
with it
• Chain ! Explore – If at the tail of a chain, leave the chain
• Finished – the task has been completed. Now it can move on to other tasks, such as object
transport.
• Explore! Finished – if the destination is really close.
• Explore ! Search – no chain member is in sight. This will only be caused by an error or fault.
Simple S-bot
An s-bot is a small mobile autonomous robot with self-assembling capabilities.
It weighs 700 g and its main body has a diameter of about 12 cm.
The traction system is composed of both tracks and wheels.
The gripper is mounted on Turret as some freedom for lifting object.
Gripper used for connecting to S-Bots and objects.
Four proximity sensors placed under the chassis (ground sensors), torque and traction sensor.
Eight light sensors uniformly distributed around the turret ,two humidity sensor & 3-axis accelerometer.
Each robot is also equipped with sensors and devices to detect and communicate with each other.
Consists of transmitter, receiver ,camera, LED, microphone, loudspeaker.
Types of communication
Any task requires communication between the robots therefore the principle is that each s-bot in the group displays a simple periodic behavior. It means their oscillations are in phase with each other. In order to communicate with each other, s-bots produce a continuous tone with fixed frequency and intensity. It is perceived by every robot in arena. The tone is perceived in a binary way.
Communication actually takes place using ad-hoc wireless communications
like RF or I
Two types of communication 1.Interaction via sensing 2.Interaction via communication
Interaction via sensing
if two robots interacting to pull a stick and sensing each other’s action in a limited way , this work is considered as “interaction via sensing”.
Interaction via sensing requires the discrimination of other robots from the environment objects, also called as the kin recognition.
kin recognition is considered as a kind of minimalist communication mechanism since just by discriminating the kin and observing their behaviors (without explicit communication),the robots can manage to solve several problems (e.g. flocking, chain formation and cooperative stick pulling) in swarm robotics.
Interaction via communication
robots broadcasts information packages directly or switches on/off a light around them to show their state, these studies are considered to be the type of “interaction via communication”
If the sender in the interaction aims to give information to other robots intentionally then that study is categorized as “interaction via communication”
Co-ordination
Centralized: the organization of a system having a robotic agent (a leader) that is in charge of organizing the work of the other robots; the leader is involved in the decisional process for the whole team, while the other members act according to the directions of the leader.
Decentralized: the organization of a system composed by robotic agents which are completely autonomous in the decisional process with respect to each other; in this class of systems a leader does not exist.
Reconfigurable swarm bots
Lattice architecture
Chain architecture
Mobile architecture
Applications
Plume tracing
Obstacle avoidance
Search and rescue
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