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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

Research on path planning of industrial robot based on improved ant colony algorithm

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基于改进蚁群算法的工业机器人路径规划研究

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Apr.2023, Vol.46 No.4: 463-467, 534

DOI: 10.3969/j.issn.1003-5060.2023.04.006

Authors

ZHU Min, XIAO Yang, ZANG Zhaoyu

(School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: Aiming at the problems of ant colony algorithm in manipulator path planning, such as long path and slow convergence speed, this paper proposes an improved ant colony algorithm. In this algorithm, the ant colony is divided into outer ant colony and inner ant colony, different heuristic functions are designed to improve the search efficiency, and the safety factor is introduced to improve the safety of the manipulator movement process. The outer ant colony is used to initialize pheromone and guide the inner ant colony to conduct global optimization. In order to strengthen the optimization ability of high-quality population, the mechanism of prey allocation of wolves is introduced into the pheromone update principle. It can improve the concentration of some path pheromones and prevent the algorithm from falling into local optimum. Finally, the effective path at the end of the manipulator is transformed into an optimal position and attitude path of the manipulator through inverse kinematics operation and collision detection. The simulation experiments show that the algorithm can plan a path to avoid obstacles for the manipulator in different environments that meets the motion requirements.

Keywords: improved ant colony algorithm; manipulator; path planning; safety factor; collision detection; prey allocation mechanism of wolves

作者信息

朱敏，肖阳，臧昭宇

（合肥工业大学电气与自动化工程学院，安徽合肥230009）

摘要和关键词

摘要: 针对蚁群算法在机械臂路径规划中存在的问题,如路径过长、收敛速度慢等,文章提出一种改进的蚁群算法。首先将蚁群分为外层蚁群和内层蚁群,分别设计不同的启发函数来提高搜索效率,并引入安全因子提高机械臂运动过程的安全性,利用外层蚁群初始化信息素,引导内层蚁群进行全局寻优;为了加强优质种群的寻优能力,在信息素更新原则中引入狼群的猎物分配机制,同时改善部分路径信息素浓度,防止算法陷入局部最优;得到的机械臂末端有效路径再经过机械臂逆运动学运算和碰撞检测,转化为一条机械臂最优位姿路径。仿真实验表明该算法能为机械臂在不同环境中规划出一条符合运动要求的避障路径。

关键词: 改进蚁群算法；机械臂；路径规划；安全因子；碰撞检测；狼群的猎物分配机制

Funding

国家自然科学基金资助项目（62073113）

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