基于多LCS和人工势场法的机器人行为控制  

Robot Behavior Control Based on Multi-LCS and the Artificial Potential Field

作　　者：邵杰[1] 杨静宇[1] 

机构地区：[1]南京理工大学计算机学院,南京210094

出　　处：《计算机科学》2011年第1期264-267,共4页Computer Science

基　　金：国家自然科学基金项目(60705020);面向移动机器人环境感知的主动学习研究资助

摘　　要：基于遗传算法的早熟收敛、局部最优解、占据较大的存储空间等缺陷,提出了一种基于多LCS和人工势场法的机器人行为控制方法,设计了特殊的适应度函数。不同算法的仿真实验结果表明多LCS和人工势场法相结合用于机器人行为控制是收敛的,很大程度上改善了遗传算法的早熟收敛和收敛速度慢等问题。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.

关 键 词：行为控制 机器人 学习分类器 覆盖算法 人工势场法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]