基于AMSSA和改进APF的电力物资仓机器人路径规划方法

Power material warehouse robot path planning method based on adaptive mutation sparrow search algorithm and improved artificial potential field method

摘要

随着电力物资仓储管理的自动化和智能化进程不断推进,取货机器人的应用日益增多。为了提高机器人在多目标路径规划的效率,提出一种基于自适应变异麻雀搜索算法和改进人工势场法的路径规划算法。首先,通过网格图法建立用于机器人路径规划的二维地图模型;之后,针对传统人工势场法中的目标点不可达和陷入局部最优问题,采取增设虚拟目标点等方法改进人工势场法;进一步,采用改进的人工势场法在起点和各个终点两两之间进行路径规划,将多目标路径规划问题转化为求解旅行商问题;然后,采用自适应变异麻雀搜索算法进行迭代寻优,找到全局最优路径;最后,利用仿真实验验证算法的有效性。实验结果表明,所提出的算法能够避免机器人陷入局部最优点,并且能够实现多目标点路径规划的全局最优。

Abstract

As the automation and intelligence of power material warehousing management continue to advance, the application of picking robots is increasingly widespread. To improve the efficiency of robots in multi-objective path planning, a path planning algorithm based on an adaptive mutation sparrow search algorithm and an improved artificial potential field method is proposed. First, a two-dimensional map model for robot path planning is established using the grid-based method. Then, to address the issues of unreachable target points and getting stuck in local optima in traditional artificial potential field methods, improvements are made by adding virtual target points and other techniques. Furthermore, the improved artificial potential field method is applied to perform path planning between the starting point and each of the target points, transforming the multi-objective path planning problem into a traveling salesman problem. Next, an adaptive mutation sparrow search algorithm is used for iterative optimization to find the global optimal path. Finally, simulation experiments are conducted to verify the effectiveness of the algorithm. The experimental results show that the proposed algorithm can avoid the robot getting stuck in local optima and achieve the global optimal path planning for multiple target points.

关键词

电力物资仓 / 机器人 / 路径规划 / 自适应麻雀优化算法 / 改进人工势场法

Key words

power supply warehouse / robot / path planning / adaptive sparrow optimization algorithm / improved artificial potential field method

[1] Qin H, Shao S, Wang T, et al. Review of autonomous path planning algorithms for mobile robots[J]. Drones, 2023, 7(3):211.
[2] Liu L, Wang X, Yang X, et al. Path planning techniques for mobile robots: Review and prospect[J]. Expert Systems with Applications, 2023(227):120254.
[3] Qin P, Liu F, Guo Z, et al. Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method[J]. Transactions of the Institute of Measurement and Control, 2024, 46(4): 799-812.
[4] Wu H, Zhang Y, Huang L, et al. Research on vehicle obstacle avoidance path planning based on APF-PSO[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2023, 237(6): 1391-1405.
[5] Li D, Wang L, Cai J, et al. Research on path planning of mobile robot based on improved genetic algorithm[J]. International Journal of Modeling, Simulation, and Scientific Computing, 2023, 14(6): 2341030.
[6] 聂清彬. 基于动态自适应蚁群优化算法的移动机器人路径规划[J]. 计算机应用, 2024, 44(增刊1):351-354.
[7] 胡啸, 张呈越, 卞炜, 王健安, 董朋涛. 基于改进哈里斯鹰优化算法的动态路径规划研究[J]. 控制工程, 2024, 31(4):591-600.
[8] Lin S, Liu A, Wang J, et al. An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse[J]. Journal of Computational Science, 2023(67): 101938.
[9] 胡晓虎, 白永忠, 李旭, 等. 基于双向RRT的风电场巡检路径规划算法[J]. 自动化技术与应用, 2025, 44(3):25-28.
[10] 王霄. 基于改进灰狼优化算法的仓储物流机器人路径规划方法[J]. 自动化技术与应用, 2022, 41(12):23-27.
[11] 刘元华, 李超群, 郭乙运. 仓储物流机器人多启发蚁群路径规划与避障[J]. 机械设计与制造, 2024(11):296-300.
[12] Zheng Y, Shao X, Chen Z, et al. Improvements on the virtual obstacle method[J]. International Journal of Advanced Robotic Systems, 2020, 17(2):1-9.
[13] 纪苏宁, 曹景胜, 刘世江, 等. 基于改进人工势场法的避障路径规划研究[J]. 现代电子技术, 2025, 48(1):117-122.
[14] Rostami S M H, Sangaiah A K, Wang J, et al. Obstacle avoidance of mobile robots using modified artificial potential field algorithm[J]. EURASIP Journal on Wireless Communications and Networking, 2019(1): 1-19.
[15] 唐延强, 李成海, 宋亚飞, 等. 自适应变异麻雀搜索优化算法[J]. 北京航空航天大学学报, 2023, 49(3): 681-692.
[16] 李江华, 王鹏晖, 李伟. 一种混合多策略改进的麻雀搜索算法[J]. 计算机工程与科学, 2024, 46(2):303-315.
[17] You D, Kang S, Yu J, et al. Path Planning of Robot Based on Improved Multi-Strategy Fusion Whale Algorithm[J]. Electronics, 2024, 13(17): 3443.
[18] Miao H, Qiu Z, Zeng C. Multi-strategy improved slime mould algorithm and its application in optimal operation of cascade reservoirs[J]. Water Resources Management, 2022, 36(9): 3029-3048.
[19] Hamad R K, Rashid T A. GOOSE algorithm: a powerful optimization tool for real-world engineering challenges and beyond[J]. Evolving Systems, 2024, 15(4):1249-1274.

基金资助

国网山东省电力公司科技项目(520608240008)