面向连续弯道的改进人工势场建模与动态避障路径规划

doi:10.3901/JME.260054

机械工程学报 ›› 2026, Vol. 62 ›› Issue (2): 271-282.doi: 10.3901/JME.260054

• 运载工程 • 上一篇

扫码分享

面向连续弯道的改进人工势场建模与动态避障路径规划

张志勇^1,2, 姜志昊¹, 胡林¹, 黄彩霞³

1. 长沙理工大学汽车与机械工程学院长沙 410114;
2. 长沙理工大学智能道路与车路协同湖南省重点实验室长沙 410114;
3. 湖南工程学院机械工程学院湘潭 411104

收稿日期:2025-01-20 修回日期:2025-09-13 发布日期:2026-03-02
作者简介:张志勇,男,1976年出生,博士,教授。主要研究方向为智能汽车主动安全控制,车辆动力学及控制及电动汽车能量管理。E-mail:zzy04@163.com;姜志昊,男,1998年出生,硕士研究生。主要研究方向为智能汽车,轨迹规划与跟踪。E-mail:931152189@qq.com
基金资助:
国家杰出青年科学基金(52325211),国家自然科学基金(52472399),湖南省科技创新计划(2025RC1052),湖南省普通高等学校科技创新团队(2023CT02),湖南省创新研究群体(2025JJ10006)资助项目。

Improved Artificial Potential Field Modeling and Dynamic Obstacle Avoidance Path Planning for Continuous Curves

ZHANG Zhiyong^1,2, JIANG Zhihao¹, HU Lin¹, HUANG Caixia³

1. School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114;
2. Hunan Key Laboratory of Smart Roadways and Cooperative Vehicle-infrastructure Systems, Changsha University of Science & Technology, Changsha 410114;
3. School of Mechanical Engineering, Hunan Institute of Technology, Xiangtan 411104

Received:2025-01-20 Revised:2025-09-13 Published:2026-03-02

摘要/Abstract

摘要： 传统人工势场(Artificial potential field,APF)算法在弯道进行避障路径规划时，存在道路APF未体现车速对安全风险的影响，障碍物APF容易导致规划的路径波动的缺陷。为了弥补这些缺陷，建立改进的交通环境综合APF。针对车辆在高速过弯时可能因偏离车道中心线而发生碰撞的风险，提出根据车速动态调节道路边界约束强度的方法，在Frenet坐标中建立改进道路APF。为准确描述车辆和障碍物相对速度表征的碰撞风险，在分析传统障碍物APF的缺陷基础上，基于向量法定义相对接近系数，建立基于相对接近系数和相对位置关系，同时适应静态和动态障碍物的改进障碍物APF。为验证建立的改进APF性能，在Carsim/Simulink联合仿真环境下进行对比分析。结果表明，所提出的改进道路APF能有效减小车辆高速过弯时偏离车道中心线的程度，显著减小发生道路边界碰撞的风险；改进障碍物APF能根据相对接近系数动态调节分布范围，平滑地规划出避障路径；由上述两个APF构成的交通环境综合APF能更安全地引导车辆在连续弯道上行驶与避障。

关键词: 智能汽车, 路径规划, 人工势场, 避障

Abstract: When employing the conventional artificial potential field(APF) algorithm for obstacle avoidance path planning on curves, the road APF does not reflect the impact of vehicle speed on safety risk, and the obstacle APF is easy to cause the fluctuation of the planned path. To address these deficiencies, an improved and comprehensive APF of traffic environment has been developed. Specifically, considering the risk of collisions due to vehicles deviating from the centerline of their lanes at high velocities during turns, a method has been proposed that dynamically adjusts the road boundary constraints based on vehicle velocity. This is implemented within the Frenet coordinate system to achieve an improved road APF. To accurately represent the collision risk characterized by the relative velocity between the vehicle and obstacles, the shortcomings of the traditional obstacle APF are analyzed. Based on a vector approach, a relative proximity coefficient is defined, leading to the establishment of an improved obstacle APF that considers both the relative proximity coefficient and the relative positional relationship, adapting to both static and dynamic obstacles. To validate the performance of these improved APFs, comparative analyses were conducted in a Carsim/Simulink joint simulation environment. The results indicate that the proposed improved road APF effectively reduces the extent to which vehicles deviate from the centerline of their lanes during high-speed turns, significantly lowering the risk of collisions with road boundaries. The improved obstacle APF, adjusting its distribution range dynamically based on the relative proximity coefficient, smoothly plans obstacle avoidance paths. The comprehensive APF of traffic environment, composed of the aforementioned two APFs, can more safely guide vehicles in navigating and avoiding obstacles on continuous curves.

Key words: intelligent vehicle, path planning, artificial potential field, obstacle avoidance

中图分类号:

U469

张志勇, 姜志昊, 胡林, 黄彩霞. 面向连续弯道的改进人工势场建模与动态避障路径规划[J]. 机械工程学报, 2026, 62(2): 271-282.

ZHANG Zhiyong, JIANG Zhihao, HU Lin, HUANG Caixia. Improved Artificial Potential Field Modeling and Dynamic Obstacle Avoidance Path Planning for Continuous Curves[J]. Journal of Mechanical Engineering, 2026, 62(2): 271-282.

参考文献

[1] 李荣粲,庄伟超,殷国栋,等.自动驾驶赛车路径与车速协同规划方法[J].机械工程学报,2022,58(10):200-208.LI Rongcan,ZHUANG Weichao,YIN Guodong,et al.Simultaneous path and speed planning of driverless racing car[J]. Journal of Mechanical Engineering,2022,58(10):200-208.
[2] SONG F,QIAN Y,YAN W. Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm[J]. Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2021,235(14):3416-3430.
[3] CLAUSSMANN L,REVILLOUD M,GRUYER D,et al.A review of motion planning for highway autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems,2019,21(5):1826-1848.
[4] 胡杰,朱琪,陈锐鹏,等.引入必经点约束的智能汽车全局路径规划[J].汽车工程,2023,45(3):350-360.HU Jie,ZHU Qi,CHEN Ruipeng,et al. Global path planning of intelligent vehicle with must-pass nodes[J].Automotive Engineering,2023,45(3):350-360.
[5] CUI Q,DING R,WU X,et al. A new strategy for rear-end collision avoidance via autonomous steering and differential braking in highway driving[J]. Vehicle System Dynamics,2020,58(6):955-986.
[6] DIXIT S,FALLAH S,MONTANARO U,et al. Trajectory planning and tracking for autonomous overtaking:State-of-the-art and future prospects[J]. Annual Reviews in Control,2018,45:76-86.
[7] WANG J,YAN Y,ZHANG K,et al. Path planning on large curvature roads using driver-vehicle-road system based on the kinematic vehicle model[J]. IEEE Transactions on Vehicular Technology,2021,71(1):311-325.
[8] 王其东,魏振亚,陈无畏,等.基于参数时变人工势场的车道保持协调控制[J].机械工程学报,2018,54(18):105-114.WANG Qidong,WEI Zhenya,CHEN Wuwei,et al. Lane keeping coordination control based on parameters-varing artificial potential field[J]. Journal of Mechanical Engineering,2018,54(18):105-114.
[9] 阳鑫,唐小林,杨凯,等.极限工况下无人驾驶车辆运动规划策略研究[J].机械工程学报,2022,58(22):349-359.YANG Xin,TANG Xiaolin,YANG Kai,et al. Research on the motion planning strategy for autonomous vehicles in extreme conditions[J]. Journal of Mechanical Engineering,2022,58(22):349-359.
[10] 王明强,王震坡,张雷.基于碰撞风险评估的智能汽车局部路径规划方法研究[J].机械工程学报,2021,57(10):28-41.WANG Mingqiang,WANG Zhenpo,ZHANG Lei,Local path planning for intelligent vehicles based on collision risk evaluation[J]. Journal of Mechanical Engineering,2021,57(10):28-41.
[11] RASEKHIPOUR Y,KHAJEPOUR A,CHEN S K,et al.A potential field-based model predictive path-planning controller for autonomous road vehicles[J]. IEEE Transactions on Intelligent Transportation Systems,2017,18(5):1255-1267.
[12] 朱乃宣,高振海,胡宏宇,等.基于交通风险评估的个性化换道触发研究[J].汽车工程,2021,43(9):1314-1321.ZHU Naixuan,GAO Zhenhai,HU Hongyu,et al.Research on personalized lane change triggering based on traffic risk assessment[J]. Automotive Engineering,2021,43(9):1314-1321.
[13] YUE M,FANG C,ZHANG H,et al. Adaptive authority allocation-based driver-automation shared control for autonomous vehicles[J]. Accident Analysis&Prevention,2021,160:106301.
[14] 张家旭,王晨,赵健.基于改进人工势场法的汽车弯道超车路径规划与跟踪控制[J].汽车工程,2021,43(4):546-552.ZHANG Jiaxu,WANG Chen,ZHAO Jian. Path planning and tracking control for vehicle overtaking on curve based on modified artificial potential field method[J].Automotive Engineering,2021,43(4):546-552.
[15] HU X,CHEN L,TANG B,et al. Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles[J]. Mechanical Systems and Signal Processing,2018,100:482-500.
[16] ZUO Z,YANG X,LI Z,et al. MPC-based cooperative control strategy of path planning and trajectory tracking for intelligent vehicles[J]. IEEE Transactions on Intelligent Vehicles,2020,6(3):513-522.
[17] ZHENG L,ZENG P,YANG W,et al. Bézier curvebased trajectory planning for autonomous vehicles with collision avoidance[J]. IET Intelligent Transport Systems,2020,14(13):1882-1891.
[18] MANEL A,RODOLFO O,MICHAEL B,et al. A MPC combined decision making and trajectory planning for autonomous vehicle collision avoidance[J]. IEEE Transactions on Intelligent Transportation Systems,2022,23(12):24805-24817.
[19] WANG H,HUANG Y J,KHAJEPOUR A,et al. Crash mitigation in motion planning for autonomous vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2019,20(9):3313-3323.
[20] 杜荣华,胡鸿飞,高凯,等.基于变预测时域MPC的自动驾驶汽车轨迹跟踪控制研究[J].机械工程学报,2022,58(24):275-288.DU Ronghua,HU Hongfei,GAO Kai,et al. Research on trajectory tracking control of autonomous vehicle based on MPC with variable predictive horizon[J]. Journal of Mechanical Engineering,2022,58(24):275-288.
[21] 高振刚,陈无畏,谈东奎,等.考虑驾驶员操纵失误的车道偏离辅助人机协同控制[J].机械工程学报,2019,55(16):91-103.GAO Zhengang,CHEN Wuwei,TAN Dongkui,et al.Human-machine cooperative lane departure assist control considering driver manipulate failure[J]. Journal of Mechanical Engineering,2019,55(16):91-103.
[22] 王艺,蔡英凤,陈龙,等.基于模型预测控制的智能网联汽车路径跟踪控制器设计[J].机械工程学报,2019,55(8):136-144.WANG Yi,CAI Yingfeng,CHEN Long,et al. Design of intelligent and connected vehicle path tracking controller based on model predictive control[J]. Journal of Mechanical Engineering,2019,55(8):136-144.
[23] 贺伊琳,马建,杨舒凯,等.融合预瞄特性的智能电动汽车稳定性模型预测控制研究[J].汽车工程,2023,45(5):719-734.HE Yilin,MA Jian,YANG Shukai,et al. Research on stability model predictive control of intelligent electric vehicle with preview characteristics[J]. Automotive Engineering,2023,45(5):719-734.
[24] WEI H,ZHAO W,AI Q,et al. Deep reinforcement learning based active safety control for distributed drive electric vehicles[J]. IET Intelligent Transport Systems,2022,16(6):813-824.
[25] 中华人民共和国交通运输部. JTG B01-2014公路工程技术标准[S].北京:人民交通出版社2014.Ministry of Transport of the People’s Republic of China.JTG B01-2014 Technical standard of highway engineering[S]. Beijing:China Communications Press2014.

面向连续弯道的改进人工势场建模与动态避障路径规划

Improved Artificial Potential Field Modeling and Dynamic Obstacle Avoidance Path Planning for Continuous Curves

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	武星, 李杨志, 臧铁钢, 孟昭旭, 陈俊哲, 王晨涛. 基于Voronoi骨架的移动机器人融合路径规划[J]. 机械工程学报, 2025, 61(5): 165-177.
[2]	陈祉旭, 陈远航, 李晨星, 杨春利. 复杂叶片修复的电弧熔丝沉积姿态控制方法研究[J]. 机械工程学报, 2025, 61(22): 47-56.
[3]	姚静, 李曼迪, 张熙堃, 郭琪, 李想, 张昊. 增材制造流道低压损短路径设计优化方法[J]. 机械工程学报, 2025, 61(22): 271-281.
[4]	李通甲, 臧鹏翔, 郭为忠. 新型磁吸附轮式机器人定位、规划与控制算法[J]. 机械工程学报, 2025, 61(21): 60-74.
[5]	赵靖, 王伟达, 杨超, 李颖, 项昌乐, 向真, 昌磊. 一种兼顾运行距离与能耗的飞行车辆多模态任务路线规划方法[J]. 机械工程学报, 2025, 61(2): 222-235.
[6]	朱鹏兴, 胡建军, 李嘉佳, 彭航, 王鑫. 考虑驾驶风格不确定性的插电式混合动力汽车个性化生态自适应巡航协同优化方法[J]. 机械工程学报, 2025, 61(18): 214-229.
[7]	邓建新, 袁邦颐, 黄秋林, 丁度坤, 辛曼玉, 刘光明. 基于工业机器人的复杂曲面磨抛关键技术综述[J]. 机械工程学报, 2024, 60(7): 1-21.
[8]	李鹏, 王刘银, 王刚, 马书根. 蛇形机器人全身避障策略设计与实验研究[J]. 机械工程学报, 2024, 60(21): 4-13.
[9]	张伟民, 徐森生, 张月. 基于改进A^*算法的室内巡检机器人路径规划研究[J]. 机械工程学报, 2024, 60(20): 315-326.
[10]	王晓飞, 许家忠, 丁亮, 黄成, 杨怀广, 刘美军. 趋向性锁链式路径规划算法[J]. 机械工程学报, 2024, 60(19): 53-61.
[11]	徐文琳, 彭羽, 何智成, 姜潮. 复杂路径规划的机构分区运动学拓扑构型设计[J]. 机械工程学报, 2024, 60(11): 62-73.
[12]	闫守鑫, 王伟, 苏鹏飞, 贠超. 大型复杂锻件打磨路径的智能化规划方法[J]. 机械工程学报, 2024, 60(11): 216-225.
[13]	林晨, 何智成, 黄怡菲, 林智桂, 付广, 黄晋. 多级参数融合网络的驾驶场景目标检测方法研究[J]. 机械工程学报, 2024, 60(10): 64-75.
[14]	李玖法, 邹博文, 任玥. 考虑车-路交互作用的车辆轨迹预测算法研究[J]. 机械工程学报, 2024, 60(10): 76-85.
[15]	胡林, 杨冬兆, 张新, 章杰, 廖家才. 基于DQP-LMPC的智能车超车换道动态路径规划[J]. 机械工程学报, 2024, 60(10): 171-181.