Path Planning for Mobile Robots Based on Improved Artificial Potential Field Algorithm

doi:10.16182/j.issn1004731x.joss.24-0665

Abstract

Abstract:

In view of the problems of unreachable target areas and easy local minima in traditional artificial potential field methods, an improved artificial potential field method was proposed. The improved algorithm optimized the repulsive field function by introducing obstacle angle factors and distance factors to control the repulsive force magnitude. At the same time, an additional repulsive force towards the target point was added to solve the problem of unreachable target areas in traditional algorithms. When the robot fell into a local minimum, by introducing turning towards obstacles and turning factors to accurately apply escape forces to the robot, the problem of robots easily falling into local minima was effectively solved. Simulation experiments show that in both conventional and complex environments, the improved algorithm can overcome the above problems and plan a smooth path. Compared with other algorithms, the path quality of the improved algorithm is better, and the planning time is shorter.

Key words: path planning, artificial potential field method, mobile robot, local minima, escape force

CLC Number:

TP242

Zhang Chi, Wei Wei. Path Planning for Mobile Robots Based on Improved Artificial Potential Field Algorithm[J]. Journal of System Simulation, 2025, 37(11): 2918-2926.

Figures/Tables 17

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 1

Parameter Data

参数名称	数量/大小
引力增益系数 $k a t t$	1
斥力增益系数 $k r e p$	10
障碍物影响距离 $ρ o$	3
步长	0.01
障碍物数量	9

Table 1

Fig. 10

Table 2

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

References 17

[1]	林韩熙, 向丹, 欧阳剑, 等. 移动机器人路径规划算法的研究综述[J]. 计算机工程与应用, 2021, 57(18): 38-48.
	Lin Hanxi, Xiang Dan, Ouyang Jian, et al. Review of Path Planning Algorithms for Mobile Robots[J]. Computer Engineering and Applications, 2021, 57(18): 38-48.
[2]	李晓旭, 马兴录, 王先鹏. 移动机器人路径规划算法综述[J]. 计算机测量与控制, 2022, 30(7): 9-19.
	Li Xiaoxu, Ma Xinglu, Wang Xianpeng. A Survey of Path Planning Algorithms for Mobile Robots[J]. Computer Measurement & Control, 2022, 30(7): 9-19.
[3]	Zhang Huixia, Tao Yadong, Zhu Wenliang. Global Path Planning of Unmanned Surface Vehicle Based on Improved A-star Algorithm[J]. Sensors, 2023, 23(14): 6647.
[4]	Alshammrei Shaher, Boubaker Sahbi, Kolsi Lioua. Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance[J]. Computers, Materials and Continua, 2022, 72(3): 5939-5954.
[5]	张瑞, 周丽, 刘正洋. 融合RRT*与DWA算法的移动机器人动态路径规划[J]. 系统仿真学报, 2024, 36(4): 957-968.
	Zhang Rui, Zhou Li, Liu Zhengyang. Dynamic Path Planning for Mobile Robot Based on RRT* and Dynamic Window Approach[J]. Journal of System Simulation, 2024, 36(4): 957-968.
[6]	马宗方, 张琳旋, 宋琳, 等. 基于改进动态窗口的车库AGV路径规划及仿真[J]. 系统仿真学报, 2024, 36(10): 2265-2276.
	Ma Zongfang, Zhang Linxuan, Song Lin, et al. Garage AGV Path Planning and Simulation Based on Improved DWA[J]. Journal of System Simulation, 2024, 36(10): 2265-2276.
[7]	吕刚, 吕金壮, 邓军, 等. 部分环境信息未知下的机器人动态路径规划算法[J]. 自动化与仪器仪表, 2022(3): 186-190.
	Gang Lü, Jinzhuang Lü, Deng Jun, et al. Dynamic Path Planning Algorithm for Robots with Partially Unknown Environmental Information[J]. Automation & Instrumentation, 2022(3): 186-190.
[8]	Lin Zenan, Yue Ming, Chen Guangyi, et al. Path Planning of Mobile Robot with PSO-based APF and Fuzzy-based DWA Subject to Moving Obstacles[J]. Transactions of the Institute of Measurement and Control, 2022, 44(1): 121-132.
[9]	Wang Pengwei, Gao Song, Li Liang, et al. Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm[J]. Energies, 2019, 12(12): 2342.
[10]	Tawseef Ahmed Teli, Arif Wani M. A Fuzzy Based Local Minima Avoidance Path Planning in Autonomous Robots[J]. International Journal of Information Technology, 2021, 13(1): 33-40.
[11]	Orozco-Rosas Ulises, Montiel Oscar, Sepúlveda Roberto. Mobile Robot Path Planning Using Membrane Evolutionary Artificial Potential Field[J]. Applied Soft Computing, 2019, 77: 236-251.
[12]	Wang Siming, Zhao Tiantian, Li Weijie. Mobile Robot Path Planning Based on Improved Artificial Potential Field Method[C]//2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE). Piscataway: IEEE, 2018: 29-33.
[13]	周克帅, 范平清. 改进A*算法与人工势场算法移动机器人路径规划[J]. 电子器件, 2021, 44(2): 368-374.
	Zhou Keshuai, Fan Pingqing. Improved A* Algorithm and Artificial Potential Field Algorithm for Mobile Robot Path Planning[J]. Chinese Journal of Electron Devices, 2021, 44(2): 368-374.
[14]	徐小强, 王明勇, 冒燕. 基于改进人工势场法的移动机器人路径规划[J]. 计算机应用, 2020, 40(12): 3508-3512.
	Xu Xiaoqiang, Wang Mingyong, Mao Yan. Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method[J]. Journal of Computer Applications, 2020, 40(12): 3508-3512.
[15]	涂柯, 侯宏录, 苏炜. 改进人工势场法的无人机避障路径规划[J]. 西安工业大学学报, 2022, 42(2): 170-177.
	Tu Ke, Hou Honglu, Su Wei. Obstacle Avoidance Path Planning of UAV Based on Improved Artificial Potential Field Method[J]. Journal of Xi'an Technological University, 2022, 42(2): 170-177.
[16]	Khatib O. Real-time Obstacle Avoidance for Manipulators and Mobile Robots[J]. The International Journal of Robotics Research, 1986, 5(1): 90-98.
[17]	Priyadarshi Biplab Kumar, Rawat Himanshu, Parhi Dayal R. Path Planning of Humanoids Based on Artificial Potential Field Method in Unknown Environments[J]. Expert Systems, 2019, 36(2): e12360.

算法	路径长度/m	迭代次数	时间/s
传统人工势场法	—	3 001	0.207
文献[15]改进算法	9.57	1 106	0.175
本文改进算法	9.03	904	0.143