基于改进人工势场的路径规划与跟踪控制

doi:10.3969/j.issn.1674-0696.2020.09.04

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (2212 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要: 针对传统人工势场法应用于无人驾驶车辆路径规划存在的道路势场不完善问题,提出了改进的人工势场模型，用于场算法获取避障轨迹。笔者首先结合势能在道路变化来改进引力势场，增加类椭圆形斥力势场代替传统斥力势场，增加道路边界斥力势场保障车辆不会驶出边界，增加障碍物速度势场来完善障碍物的合理性，同时建立3自由度车辆动力学模型，运用模型预测控制算法进行跟踪控制。研究结果表明：改进的人工势具有良好的避障路径规划，同时满足各方面动力学约束，转角变化在±3°以内，最大侧向加速度为4.54 m/s2＜0.67 μg，保证规划路径具有行驶的稳定性和舒适性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李军
	李古月

关键词 ：车辆工程, 无人驾驶车辆, 轨迹跟踪, 人工势场, 模型预测

Abstract：In view of the imperfection of road potential field when the traditional artificial potential field method was applied in driverless vehicle path planning, an improved artificial potential field model was proposed to obtain the obstacle avoidance trajectory. Firstly, combined with the change of potential energy in the road, the gravitational potential field was improved; the elliptical repulsion potential field was increased to replace the traditional repulsion potential field; the road boundary repulsion potential field was increased to ensure that the vehicle would not drive out of the boundary; the speed potential field of obstacles was increased to improve the rationality of the obstacle. At the same time, a three-degree-of-freedom vehicle dynamics model was established, and the model predictive control algorithm was used for tracking control. The research results show that the improved artificial potential has a good obstacle avoidance path planning, and meets the dynamic constraints in all aspects at the same time. The rotation angle change is within ±3°, and the maximum lateral acceleration is 4.54 m/s2<0.67 μg, which ensures the driving stability and comfort of the planned path.

Key words ： vehicle engineering, unmanned vehicle, trajectory tracking, artificial potential field, model prediction

收稿日期: 2019-01-08

PACS:

O 319.56

基金资助:重庆市轨道交通车辆系统集成与控制重庆市重点实验室项目(CSTC2015yfpt-zdsys30001)；重庆市工程技术中心项目 (cstc2015yfpt_gcjsyjzx011)

作者简介: 李军(1964—)，男，重庆人，教授，博士，主要从事新能源汽车和智能车辆与控制方面的研究。E-mail: cqleejun@163.com 通信作者：李古月(1994—)，男，湖北随州人，硕士研究生，主要从事智能汽车辅助驾驶方面的研究。E-mail: cqlgy666@163.com

引用本文:

李军，李古月. 基于改进人工势场的路径规划与跟踪控制[J]. 重庆交通大学学报（自然科学版）, 2020, 39(09): 25-30.
LI Jun, LI Guyue. Path Planning and Tracking Control Based on Improved Artificial Potential Field. Journal of Chongqing Jiaotong University(Natural Science), 2020, 39(09): 25-30.

链接本文:

http://xbzk.cqjtu.edu.cn/CN/10.3969/j.issn.1674-0696.2020.09.04 或 http://xbzk.cqjtu.edu.cn/CN/Y2020/V39/I09/25

［1］ KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots ［J］. The International Journal of Robotics Research, 1986, 5(1):90-98.
［2］张殿富, 刘福. 基于人工势场法的路径规划方法研究及展望［J］. 计算机工程与科学，2013，35(6)：88-95.
ZHANG Dianfu, LIU Fu. Research and development trend of path planning based on artificial potential field method ［J］. Computer Engineering and Science, 2013, 35
(6): 88-95.
［3］霍凤财,迟金,黄梓健,等.移动机器人路径规划算法综述［J］. 吉林大学学报(信息科学版)，2018，36(6)：639-647.
HUO Fengcai, CHI Jin, HUANG Zijian, et al. Review of path planning for mobile robots ［J］. Journal of Jilin University (Information Science Edition), 2018, 36(6):
639-647.
［4］修彩靖，陈慧. 基于改进人工势场法的无人驾驶车辆局部路径规划的研究［J］. 汽车工程，2013，35(9)：808-811.
XIU Caijing, CHEN Hui.A research on local path planning for antonomous vehicles based on improved APF method ［J］. Automotive Engineering, 2013, 35(9): 808-811.
［5］ SCHMIDT K W. Cooperative adaptive cruise control for vehicle following during lane changes ［J］. IFAC, 2017, 50(1): 12582-12587.
［6］唐志荣，冀杰，吴明阳，等. 基于改进人工势场法的车辆路径规划与跟踪［J］. 西南大学学报(自然科学版)，2018，40(6)：174-182.
TANG Zhirong, JI Jie, WU Mingyang,et al. Vehicle routing planning and tracking based on improved artificial potential field method ［J］. Journal of Southwest
University (Natural Science Edition), 2018, 40(6): 174-182.
［7］范世鹏，祁琪，路坤锋，等. 基于改进人工势场法的巡航导弹自主避障技术［J］. 北京理工大学学报，2018，38(8)：828-834.
FAN Shipeng, QI Qi, LU Kunfeng,et al. Autonomous collision avoidance technique for cruise missile based on modified artificial potential field method ［J］.
Journal of Beijing Institute of Technology, 2018, 38(8): 828-834.
［8］张希闻，肖本贤. 基于人工势场法的移动机器人动态路径规划［J］. 山东农业大学学报(自然科学版)，2018，49(6)：937-940.
ZHANG Xiwen, XIAO Benxian.The planning for dynamic path of a mobile robot based on artificial potential field method ［J］. Journal of Shandong Agricultural
University (Natural Science Edition), 2018, 49(6): 937-940.
［9］邵毅明，陈亚伟，束海波. 自动驾驶汽车的轨迹跟踪控制［J］. 重庆交通大学学报(自然科学版)，2019，38(8)：1-6.
SHAO Yiming, CHEN Yawei, SHU Haibo. Trajectory tracking control of self-driving cars ［J］. Journal of Chongqing Jiaotong University (Natural Science), 2019, 38
(8): 1-6.
［10］严国军，贲能军，顾建华，等. 基于MPC的无人驾驶拖拉机轨迹跟踪控制［J］. 重庆交通大学学报(自然科学版)，2019，38(9)：1-6.
YAN Guojun, BEN Nengjun, GU Jianhua, et al. Trajectory tracking control of driverless tractor based on MPC ［J］. Journal of Chongqing Jiaotong University (Natural
Science), 2019, 38 (9): 1-6.
［11］龚建伟，姜岩，徐威. 无人驾驶车辆模型预测控制［M］.北京：北京理工大学出版社，2014.
GONG Jianwei, JIANG Yan, XU Wei.Predictive Control of Unmanned Vehicle Model ［M］. Beijing: Beijing Institute of Technology Press, 2014.
［12］ MUHAMMAD A A. Non-liner Model Predictive Control for Autonomous Vehicle ［D］.Bcncvcnto: Univcrsitadcl Sannio, 2007.