摘要
针对蚁群算法收敛速度慢,规划路径存在冗余拐点不是最短路径等的一系列问题,提出优化的多步长蚁群算法。通过扩大机器人的视野域和活动域并加入简化算子,增加路径平滑度;通过差异化更新信息素并改进启发函数,促使机器人倾向终点移动,以提高收敛速度。应用MATLAB程序对改进后的算法与原蚁群算法和多步长蚁群算法进行仿真对比,通过多次仿真实验,优化的多步长蚁群算法效果显著。
Aiming at a series of problems such as low convergence speed and roughness of paths planned that the traditional ant colony optimization(ACO)has,an improved multi-step ant colony algorithm is proposed.In order to increase the smoothness of path,the field of view and movement of robot are expanded and the simplified operator is added.By updating pheromone differentially and improving the heuristic function to make the robot inclined to direction close to the end point,the convergency speed is accordingly increased.Comparing the improved algorithm with traditional ACO and multi-step ACO by numerous simulation experiments through MATLAB software,the improved multi-step ant colony algorithm shows great superiority.
作者
胡佳斌
王祥澍
张琪
全瑞坤
HU Jiabin;WANG Xiangshu;ZHANG Qi;QUAN Ruikun(CQU-UC Joint Co-op Institute,Chongqing University,Chongqing 400044,China;School of Electrical Engineering,Chongqing University,Chongqing 400044,China)
出处
《传感器与微系统》
CSCD
北大核心
2021年第10期121-124,共4页
Transducer and Microsystem Technologies
基金
重庆大学大学生创新创业训练计划资助项目(CQU-SRTP-2019346)
重庆市技术创新与应用发展专项项目(CSTC2019JSCX-MSXMX0004)
教育部产学合作协同育人项目(201802020041,201802211015)。
关键词
路径规划
蚁群优化算法
多步长
启发函数
简化算子
path planning
ant colony optimization(ACO)
multi-step
heuristic function
simplified operator
