Obstacles Avoidance Method for an Autonomous Mobile Robot using
Two IR Sensors

  Obstacles Avoidance Method for an Autonomous Mobile Robot using Two IR Sensors.pdf (Size: 343.48 KB / Downloads: 1)
Abstract:
The paper presents a local navigation
method for mobile robot, based on sensorial information
given by two IR sensors. These types of sensors are
simple and relatively low-cost sensing modalities to
perform navigation tasks in environments with obstacles.
On the other hand, IR sensors may be preferable due to
their faster response time and can be integrated in
structure with microcontroller.
Keywords: mobile robot, IR sensors, behaviors, obstacles
avoidance.
I. INTRODUCTION
Very often obstacles avoidance tasks rely on ultrasonic
sensors where the measuring data of the sensors are first
used to gain a local representation of the environment in
order to afterwards control the robot accordingly [1]. In
this context we have distinguish between two
fundamentally different types of representation: gridbased
representation [2],[3], where the environment is
divided into a number of cells which can be occupied or
free to a certain degree, and feature-based representation
[4], for example the environment is modeled by a set of
points, lines and planes.
In [5], image processing is used to detect perspective lines
to guide the robot along the center axis of the corridor.
Other authors have proposed to use optic flow to guide the
robot through corridors.
IR sensors are simple, commonly employed, and relatively
low-cost sensing modalities to perform the wall-following
task. Sometimes, IR sensors may be preferable to
ultrasonic sensors due to their faster response time,
narrower beam width, and lower cost. Unfortunately, the
intensity of the light detected depends on several
parameters including the surface reflectance properties,
the distance to the surface, and the relative orientation of
the emitter, the detector, and the surface. Due to single
intensity readings not providing sufficiently accurate
information about an object’s position and properties, the
recognition capabilities of simple IR sensors have been
underused in many applications. Although these devices
are inexpensive, practical, and widely available, their use
has been mostly limited to detection the presence or
absence of objects in the environment (proximity
detection) for applications such as obstacle avoidance,
counting or wall-following.
There are many papers which propose IR sensors for
autonomous mobile robot navigation. In [6] is described
the reactive exploration/discovery behavior which was
applied to a mobile robot through computer simulation in
the UMBRA simulation framework. The mobile robot
chosen for this simulation study was a two wheel
differential drive vehicle. This robot used eight infrared
sensors to detect the surrounding walls, and a compass
was simulated to help the robot turns to the desired
orientations and travel in a straight line.
The paper [7] describes an incremental evolutionary
approach used in the development of a suitable neural
controller for achieving robust obstacle avoidance
behavior, which is then further fine-tuned towards a wall
following one for a simple mobile robot. Obstacles
avoidance is based on data acquired from 8 IR sensors.
In [8] is proposed a homing system based on cheap IR
sensors that allows docking a mobile robot at the docking
station, for automatic recharging or another operations.
The homing system is composed of an infrared transmitter
and an infrared receiver. The former is located at the
center of the docking station and latter at the center of the
robot.
II. PRESENTATION OF THE OBSTACLES AVOIDANCE METHOD
An autonomous mobile robot (MR) equipped with two IR
proximity sensors is considered. The IR sensors (LS in the
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left, RS in the right) are mounted in front of the robot,
having the orientation like in Fig. 1. The sensors are
composed from IR emitter and receiver, and can be
controlled so that their covered distance is on three levels
(L1, L2, L3 and R1, R2, R3 respectively). On time of the
robot moving, that distances will be alternatively set up, in
increasing order.
Practically, the robot moving algorithm is based on data
acquired from the sensors. In order to plan the mobile
robot actions, the flowchart presented in Fig. 2, is used. If
neither sensor detects an obstacle then the robot moving
will be at maximum speed. When one (or both) sensor
detects one (or more) obstacle, at level L3 and/or R3, then
the robot moving will be at medium speed. When one (or
both) sensor detects one (or more) obstacle, at level L2
and/or R2, then the robot moving will be at slow speed. If
one (or both) sensor detects one (or more) obstacle, at
level L1 and/or R1, then the robot will turn left or right,
depending on the obstacles position and their mission.
Some possible situations are presented in Fig. 3.
Depending of the obstacles position the robot will sets its
speed to maximum, medium or minimum. When the
obstacles is very closed the robot, it will turn left or right,
in order to avoid it.
A more detailed presentation of the robot behaviors,
which must be activated, depending on possible situations,
is presented in Table 1.
The 1 logical signal indicates presence of one (or more)
obstacles and 0 logical signal is considered like a free way
in that area.
The algorithm can be adapted for situation in which the
robot must be moving toward a target, avoiding the
obstacles. Of course, in this situation the robot must be
equipped with a global navigation system.
III. TESTING OF THE ALGORITM
For testing of the algorithm, the miniature mobile robot
Robby RP5 (see Fig. 4) was used [9]. As can be seen in
the image, it is equipped with two IR sensors, for obstacle
detection, each of them composed from an emitter and a
receiver.
MR
R1 L1
R2 L2
R3 L3
LS RS
Fig. 1. The positions of IR sensors.
Start
L1 and R1 =0
No
Yes
L2 and R2 =0
No
Yes
L3 and R3 =0
No
Yes
Turn
Slow speed
Medium speed
Maximum speed
Fig. 2. The actions of the mobile robot.
The distance of area covered by the sensors can be set up
on three levels: 30, 60 or 100 cm, respectively.
The locomotion system of the robot is composed from two
symmetrical trays. Both of the c.c. motors and the spur
gear transmissions are integrated therein. The wheel axles
and drive shafts are supported in sintered bearings. Two
independently controllable electric motors ensure highest
mobility of the chassis.
The robot uses the D/A converters, in this case better
referred to as PWM outputs, to switch the drive motor
voltage, so that, the speed and direction of each track is
freely controllable.
The command system of the robot is presented in Fig. 5.
The microcontroller on the robot is a computer of the CControl
series. This compact unit features universal
capabilities for measuring, controlling and steering as well
as serial data communication and data storage.
MR
R1 L1
R2 L2
R3 L3
LS RS
MR
R1 L1
R2 L2
R3 L3
LS RS
Maximum speed
Turn
MR
R1 L1
R2 L2
R3 L3
LS RS
Medium speed
MR
R1 L1
R2 L2
R3 L3
LS RS
Minimum speed
Fig. 3. Example of possible situations.
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R3 L3 R2 L2 R1 L1 Obstacles
avoidance
0 0 0 0 0 0 Very fast
movement
0 1 0 0 0 0 Fast movement
0 1 0 1 0 0 Slow movement
0 1 0 1 0 1
Turn left 45°,
then slow
movement
1 0 0 0 0 0 Fast movement
1 0 1 0 0 0 Slow movement
1 0 1 0 1 0
Turn right 45°,
then slow
movement
1 1 0 0 0 0 Slow movement
1 1 1 0 0 0 Slow movement
1 1 1 0 1 0
Turn right 45°,
then slow
movement
1 1 0 1 0 0 Slow movement
1 1 0 1 0 1
Turn left 45°,
then slow
movement
1 1 1 1 0 0 Slow movement
1 1 1 1 0 1
Turn left 45°,
then slow
movement
1 1 1 1 1 0
Turn right 45°,
then slow
movement
1 1 1 1 1 1
Turn left 90°,
then slow
movement
Tab. 1. The robot behaviors depending on obstacles
position, in case of the obstacles avoidance task.
The microprocessor allows programming in the wellknown
BASIC programming language. Through a few
lines of BASIC (simplification variant CC-BASIC) source
code the computer is able to handle a task like the "brain"
of a small autonomous mobile robot.
IR
sensors
Fig. 4. The mobile robot Robby RP5.
LS
PC
Microcontroler
M68HC05
Memory
24C65
Clock
Serial interface
RS232
Inputs Outputs
ML
MR
IR
sensors
RS
Motors
Fig. 5. The command system of Robby RP5.
To communicate with its environment, it has eight
analogue inputs, two analogue outputs and sixteen digital
port lines randomly usable as inputs or outputs.
The signals given by IR sensors (LS and RS) are directed
to microcontroller inputs and the signals commands are
used for speed control of DC motor drive. In the same
time these signals can be used to turn the mobile robot
with different turning radius.
There is an Integrated Design Environment (IDE) for the
development of application programs for the robot. The
IDE is equipped with a standard mouse-controllable
graphical user interface with drop-down menus and allows
the development of source code (Editor), translating into
machine language (Compiler) and uploading the CControl
program to the robot (Loader).
The developed BASIC program, determining the actions
and reactions of the robot, will be translated into a
sequence of command bytes by the compiler. The
commands and the related parameters may then be
transferred via serial interface to the microcontroller, and
stored into the EEPROM memory (24C65). The interface
connection between PC and robot is only necessary while
uploading the program. When the robot is programmed
(the program was transferred or uploaded into robot
memory), it may be disconnected before starting the robot.
There are commands, as part of the program, whose
execution depend on data received from LS (L1, L2, L3)
or RS (R1, R2, R3), respectively. Based on these data the
mobile robot activates one of the behaviors presented in
Table 1 or 2, for tasks like: moving along right (or left)
wall or moving toward a target, avoiding the obstacles.
The speeds for motors, in program, are between 0 and
255, so these were set up as following:
- Maximum speed (speed_L=255, speed_R=255);
- Medium speed (speed_L=150, speed_R=150);
- Minimum speed (speed_L=50, speed_R=50);
- Turn 90º (speed_L® =255, speed_R (L) =50, t);
- Turn 45º (speed_L® =255, speed_R (L) =100, t).
The algorithm was tested in a room having different type
of corners and different start positions for the robot (see
Fig. 6).
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Fig. 6. Experiments with the robot.
When the robot is moving perpendicular toward the
second wall it can avoid safety (Fig. 7.a). Some problems
can arrive in another situation, when the moving direction
is not perpendicular to the wall (Fig. 7.b).
a) b)
Fig. 7. The robot guidance in corners.
IV. CONCLUSIONS
The resulting time for a complete experiment is relatively
short. This is one of the advantages of this algorithm.
But, when the algorithm was practically tested, same
errors were registered, due to the signals emitted from one
sensor which were received by the other sensor. A
solution for that problem is using encoded signals like a
remote control uses, so it is possible to know which LED
is emitting the sensed signals.
Also, due to the function principle of IR sensors, there are
situations when a small or thin object can’t be detected.
For eliminating this disadvantage a more type of sensors
or more IR sensors can be used, but in this case our
algorithm must be modified.