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基于混合人工势场-遗传算法的移动机器人路径规划仿真研究 被引量：43

Robot Path Planning Based on Hybrid Artificial Potential Field/Genetic Algorithm
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摘要 分析了人工势场模型存在的目标不可到达问题(GNRON)和由于局部最优解的存在而产生的死锁问题,提出了一种建立在改进人工势场模型上的基于遗传算法的最优路径搜索方法。仿真结果验证了本模型的有效性,能有效的解决由于人工势场模型缺陷而带来的路径规划问题。 The problems of goals nonreachable with obstacles nearby （GNRON） and dead lock caused by local minimum were described when using potential field methods for mobile robot path planning. Thus, a new way of optimizing the path using improved potential field approach with genetic algorithm was proposed. The results of simulation verify that the new method can solve the problem effectively caused by the defects of the APF model.
作者 况菲 王耀南
机构地区 湖南大学智能自动化重点实验室
出处 《系统仿真学报》 EI CAS CSCD 北大核心 2006年第3期774-777,共4页 Journal of System Simulation
基金 国家自然科学基金项目(60375001) 教育部博士点基金项目(20030532004)
关键词 人工势场模型 移动机器人 路径规划 遗传算法 artificial potential field（APF） mobile robot path planning GA
分类号 TP242 [自动化与计算机技术—检测技术与自动化装置]
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参考文献6

  • 1S S Ge, Y J Cui. New Potential Functions for Mobile Robot Path Planning[J]. IEEE Transactions on robotics and automation(S 1046-296X), 2000, 16(5): 615-620.
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  • 6王耀南,谭文.混沌系统的遗传神经网络控制[J].物理学报,2003,52(11):2723-2728. 被引量：5

二级参考文献14

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系统仿真学报
2006年 第3期

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