融合TangentBug算法和人工势场法的移动机器人路径规划  

Mobile robot path planning integrating TangentBug algorithm and artificial potential field method

作　　者：李天国 赖于树[1] 符庆川 Li Tianguo;Lai Yushu;Fu Qingchuan(School of Electronics and Information Engineering,Chongqing Three Gorges University,Chongqing 404120,China)

机构地区：[1]重庆三峡学院电子与信息工程学院,重庆404120

出　　处：《现代计算机》2024年第17期37-43,共7页Modern Computer

摘　　要：针对应用人工势场法进行路径规划时会遭遇的局部极小值问题,提出的解决方案是融合TangentBug算法应对,融合算法在应用TangentBug算法翻越障碍物后再次应用人工势场法进行路径规划,使得融合算法较传统的虚拟目标点法和传统的TangentBug算法在避障上更有效。经过仿真验证,融合算法较传统的TangentBug算法在时间和路径平滑度上分别提升了至少77.68%和10.79%的效率,较传统虚拟目标点法路径规划更有效。In view of the local minimum problem encountered when applying the artificial potential field method for path plan⁃ning,the proposed solution is to fuse the TangentBug algorithm to deal with it.The fusion algorithm applies the artificial potential field method again for path planning after applying the TangentBug algorithm to climb over obstacles,making the fusion algorithm more effective in obstacle avoidance than the traditional virtual target point method and the traditional TangentBug algorithm.After simulation,it has been verified that the fusion algorithm has improved the efficiency by at least 77.68%and 10.79%in time and path smoothness respectively compared with the traditional TangentBug algorithm,and is more successful and more effective than the traditional virtual target point method in path planning.

关 键 词：人工势场法 局部极小值 TangentBug算法 融合算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]