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Autonomous Mobile Robot Control Using Fuzzy Logic and Genetic Algorithm
Design of efficient control algorithms for autonomous mobile robot movement in unknown and changing environment with obstacles and walls is a difficult task. There exist different strategies to design control systems to perform the robot movement. In this article possibility to use fuzzy logic, if-then rules, and genetic algorithm for autonomous mobile robot control is presented. Control using fuzzy logic is softer and better then control using if-then rules because of absence of motors speed jumps. It is shown that the rough control system can be designed using an expert knowledge. Then genetic algorithm can be used to improve the quality of the control system.
Autonomous robot systems require complex control systems. Fuzzy Logic, a mathematical system developed by Professor Lotfi Zadeh, helps to reduce the complexity of modeling nonlinear problems. In the 1990s, Motorola developed the MC68HC12 microcontroller with native Fuzzy Logic instructions. This research determined the effectiveness of the MC68HC12’s Fuzzy Logic instructions for robotic control. This research involved designing a robotic platform using the MC68HC12 and testing binary logic control systems against Fuzzy Logic control systems. The research analyzed the two systems using four criteria: (1) the size of memory required to develop the control system, (2) the ease of writing the control software, (3) how well the control system managed the functions of the robot, and (4) the overall processing power of the system. The results showed that Fuzzy Logic uses less memory than binary logic and is much easier to design, although more difficult to program initially. Fuzzy Logic can control more functions of the robot and has greater processing capabilities. The power, ease of use, and small size of Fuzzy Logic instructions make Fuzzy Logic a practical solution to autonomous robotic control systems.The expansion of robotics and microcontrollers into the facets of everyday life increases the need to develop efficient control systems. A non-traditional approach to control system design is the use of Fuzzy Logic.
Fuzzy Logic extends from the traditional crisp boundaries of Aristotelian logic (true or false) to include the concept of partial truth – having truth-values between ‘completely true’ and ‘completely false.’ Dr. Lotfi Zadeh of University of California Berkeley first introduced these fuzzy methods in 1965 [1]. These methods allow the engineer to use natural language to describe and implement the control system. Fuzzy Logic uses linguistic values to represent part of the range an ordinary crisp variable may assume [2]. For example, a variable t, that represents temperature, may vary from 0 oC to 100oC. A linguistic value, COLD may be used to represent temperatures from 0oC to 10 oC while other linguistic values represent other ranges. Fuzzy Logic makes it possible to solve complex, ill-defined problems where there is a large degree of expert knowledge or the solution is easy to describe linguistically [3].
Applications of Fuzzy Logic are appearing in many industries. Fuzzy Logic enables designers to model complex systems more quickly and effectively than traditional approaches. Consumer appliances, automobile engines, transmissions, and industrial systems are all using Fuzzy Logic techniques.
Motorola’s HC68HC12 (HC12) microcontroller incorporates several Fuzzy Logic primitives directly in its instruction set. The instruction set contains the Fuzzy Logic operations of trapezoidal membership, rule evaluation, and weighted average defuzzification. The microcontroller also includes other instructions that are helpful in Fuzzy Logic applications such as MIN / MAX instructions and table lookups [4]. Motorola’s HC12 allows the development of low-level applications that can utilize the unique features of Fuzzy Logic.
The goal of this project is to design and build an autonomous line following robot based on Fuzzy Logic techniques. The robot uses the Motorola HC68HC12 microcontroller. This project involves investigating the HC12’s Fuzzy Logic instruction set and analyzing its ability to control an autonomous robot. The robot in this project serves as a test bed for several pieces of software. These software programs include initial test scripts, several control system based on traditional logic, and several control systems based on fuzzy systems. The best classical logic and fuzzy control systems are compared in various tests. These tests involved the robot following a black line on the floor.