Robot Behavior Control Based on Multi-LCS and the Artificial Potential Field
基于多LCS和人工势场法的机器人行为控制

Due to premature convergence, local optimal solution, accounting for a larger storage space and other shortcomings of genetic algorithms, a simulated control system for robot was designed by using distributed learning classifier system and Artificial potential fields to perform complex behaviors. A set of enhanced solutions of cover detectors problem was suggested and compared with each other in order to make the simulated robot more effective in choosing the appropriate behavior and improving the performance of the robot.