Local Path Planning of Artificial Potential Field Based on Improved Ｒepulsive Model

NAVIGATE

Table of Contents

STATISTICS

Viewed873

Downloads685

Local Path Planning of Artificial Potential Field Based on Improved Ｒepulsive Model

[HTML] PDF下载 (685)

[1]CHEN Jiangyi,YIN Xiaoyong,WANG Tingting,et al.Local Path Planning of Artificial Potential Field Based on Improved Ｒepulsive Model[J].Journal of Zhengzhou University (Engineering Science),2023,44(03):85-89.[doi:10.13705/j.issn.1671-6833.2022.06.015]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 03 Page number: 85-89 Column: Public date: 2023-04-30

Title:: Local Path Planning of Artificial Potential Field Based on Improved Ｒepulsive Model

Author(s):: CHEN Jiangyi; YIN Xiaoyong; WANG Tingting; QIN Dongchen; School of Mechanical and Power Engineering, Zhengzhou University, 450001, Zhengzhou, Henan

Keywords:: improved repulsion model; local path planning; repulsive force deflection; local optimum; safe dis; tanc

CLC:: U471. 15

DOI:: 10.13705/j.issn.1671-6833.2022.06.015

Abstract:: Aim to the traditional artificial potential field algorithm was easy to fall into the local optimum, this study proposed an artificial potential field method which could improve the repulsive force model. According to the relative relationship between vehicle velocity direction and obstacle position and the distance between obstacle and road boundary, the repulsive deflection direction and deflection angle were determined to avoid vehicle falling into local optimum. The repulsive field function was redesigned to introduce the safety distance of vehicle obstacle avoidance into the scope of repulsive force. Logarithmic function and longitudinal relative distance adjustment factor were introduced into the repulsive force function to reduce the curvature and total rotation angle of the planned path. The simulation results showed that the selection of repulsive deflection angle had a direct influence on the stability and safety of the local path when the artificial potential field method of the improved repulsive model is used for local path planning, and the appropriate repulsive deflection angle could avoid the occurrence of local optimum in path planning. When the vehicle need continuous obstacle avoidance in multi-obstacle environment, the total path angle and curvature peak planned by the improved repulsive force model decreasd significantly, which could effectively improve the driving safety index.

References:: [1] 宁世发, 冯忠祥. 道路交通事故成因分析[J]. 交通标准化, 2006, 34(10): 152-155.NING S F, FENG Z X. Analysis on the cause of road traffic accidents[J]. Communications Standardization, 2006, 34(10): 152-155.
[2] 彭晓燕, 谢浩, 黄晶. 无人驾驶汽车局部路径规划算法研究[J]. 汽车工程, 2020, 42(1): 1-10.PENG X Y, XIE H, HUANG J. Research on local path planning algorithm for unmanned vehicles[J]. Automotive Engineering, 2020, 42(1): 1-10.
[3] KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[J]. The International Journal of Robotics Research, 1986, 5(1): 90-98.
[4] LI G H, YAMASHITA A, ASAMA H, et al. An efficient improved artificial potential field based regression search method for robot path planning[C]∥2012 IEEE International Conference on Mechatronics and Automation. Piscataway: IEEE, 2012: 1227-1232.
[5] 于振中, 闫继宏, 赵杰, 等. 改进人工势场法的移动机器人路径规划[J]. 哈尔滨工业大学学报, 2011, 43(1): 50-55.YU Z Z, YAN J H, ZHAO J, et al. Mobile robot path planning based on improved artificial potential field me-thod[J]. Journal of Harbin Institute of Technology, 2011, 43(1): 50-55.
[6] HUANG Y Q, HU H, LIU X Q. Obstacles avoidance of artificial potential field method with memory function in complex environment[C]∥2010 8th World Congress on Intelligent Control and Automation. Piscataway: IEEE, 2010: 6414-6418.
[7] 徐飞. 基于改进人工势场法的机器人避障及路径规划研究[J]. 计算机科学, 2016, 43(12): 293-296.XU F. Research on robot obstacle avoidance and path planning based on improved artificial potential field method[J]. Computer Science, 2016, 43(12): 293-296.
[8] FEDELE G, D′ALFONSO L, CHIARAVALLOTI F, et al. Obstacles avoidance based on switching potential functions[J]. Journal of Intelligent &Robotic Systems, 2018, 90(3/4): 387-405.
[9] 范玉龙, 黄波, 周仟, 等. 基于改进人工势场法的小车避障路径规划研究[J]. 软件导刊, 2021, 20(7): 38-42.FAN Y L, HUANG B, ZHOU Q, et al. Research on vehicle obstacle avoidance path planning based on improved artificial potential field method[J]. Software Guide, 2021, 20(7): 38-42.
[10] 张三川, 明珠. 基于主动安全的改进人工势场局部路径规划研究[J]. 郑州大学学报(工学版), 2021, 42(5): 32-36, 55.ZHANG S C, MING Z. Research on improved local path planning of artificial potential field based on active safety[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(5): 32-36, 55.
[11] 王树凤, 张钧鑫, 刘宗锋. 基于改进人工势场法的智能车辆超车路径规划研究[J]. 汽车技术, 2018(3): 5-9.WANG S F, ZHANG J X, LIU Z F. A research on overtaking lane planning for intelligent vehicles based on improved artificial potential field method[J]. Automobile Technology, 2018(3): 5-9.
[12] 谭宝成, 崔佳超. 改进人工势场法在无人车避障中的应用[J]. 西安工业大学学报, 2014, 34(12): 1007-1011, 1017.TAN B C, CUI J C. Application of improved artificial potential field method in intelligent collision avoidance system on structured road[J]. Journal of Xi′an Technological University, 2014, 34(12): 1007-1011, 1017.
[13] 胡丹丹, 张琪. 基于PER-APF算法的无人驾驶汽车换道轨迹规划[J]. 计算机测量与控制, 2022, 30(6): 229-234, 241.HU D D, ZHANG Q. Lane changing trajectory planning of driverless vehicle based on PER-APFAlgorithm[J]. Computer Measurement &Control, 2022, 30(6): 229-234, 241.
[14] 来飞, 黄超群, 董红亮. 智能汽车自动紧急避撞系统的安全距离模型及其对比分析[J]. 重庆理工大学学报(自然科学), 2020, 34(9): 39-46.LAI F, HUANG C Q, DONG H L. Safety distance model and comparative analysis for intelligent vehicle emergency collision avoidance system[J]. Journal of Chongqing University of Technology (Natural Science), 2020, 34(9): 39-46.
[15] 冀杰, 姬鹏, 彭和, 等. 面向车辆主动避撞的三维虚拟危险势能场设计[J]. 汽车工程, 2016, 38(9): 1065-1071, 1079.JI J, JI P, PENG H, et al. Design of 3D virtual dange-rous potential field for vehicle active collision avoidance[J]. Automotive Engineering, 2016, 38(9): 1065-1071, 1079.

Similar References:

Memo

Last Update: 2023-05-09