Fuzzy obstacle avoidance optimization of soccer robot based on an improved genetic algorithm

• Published in: Journal of ambient intelligence and humanized computing Vol. 11; no. 12; pp. 6187 - 6198
• Main Author: Chen, Deping
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Computational Intelligence; Control methods; Decision making; Engineering; Fuzzy control; Fuzzy logic; Genetic algorithms; Information storage; Information systems; Local optimization; Neural networks; Obstacle avoidance; Original Research; Planning; Robotics; Robotics and Automation; Robots; Sensors; Simulation; Soccer; System theory; User Interfaces and Human Computer Interaction; Soccer robot; Genetic algorithm; Fuzzy obstacle avoidance; Fuzzy reasoning
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-019-01636-0

Due to its high-tech confrontation and entertainment, soccer robots have attracted a large number of researchers to participate in it. Robot obstacle avoidance is an active research branch in the field of intelligent robot technology, and is a comprehensive subject covering multiple disciplines. The traditional obstacle avoidance method of soccer robots has the disadvantages of poor local optimization ability and slow convergence speed, and it is easy to fall into local extreme points. Based on the active obstacle avoidance strategy of local sensors and processors, this paper studies the method of automatic extraction and optimization of fuzzy rules of fuzzy path planner by genetic algorithm. This fuzzy control method does not depend on accurate environmental information, and has a small amount of computation and learning ability. A fitness function for evaluating the validity of a rule is proposed. The simulation results show that the algorithm has good applicability.