摘要
结合遗传算法优化的反演性和混沌优化方法的遍历性,基于混沌遗传算法的移动机器人路径规划方法能够有效改善遗传算法的局部搜索能力和搜索精度,避免单纯使用遗传算法规划机器人路径时容易出现的早熟收敛现象.仿真试验表明,提出的路径规划方法在稀疏环境和密集环境下均能收敛到全局最优路径,具有更强的鲁棒性.
Combing the inversion property of the genetic algorithm with the ergodic property of the chaos optimization method, the path planning method based on a chaos genetic algorithm for mobile robot can improve the local search ability and search accuracy of genetic algorithm, and then effectively avoid the common defect of early convergence when using a simple genetic algorithm in path planning problems. Simulation results show that the method can converge on the global optimum path under both sparse and dense environments and is more robust to the robot workspace.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2004年第7期880-883,共4页
Journal of Harbin Institute of Technology
基金
国家高技术研究发展计划资助项目(863-2001AA422270).
关键词
混沌遗传算法
移动机器人
路径规划
Chaos theory
Genetic algorithms
Motion planning
Probability
