基于改进人工势场法的车辆路径规划与跟踪控制
海振洋1,王健1*,李大升2,杨智勇2,牟思凯1,王云靖1,邓欢1
1.山东交通学院汽车工程学院,山东 济南 250357;2.山东鲁阔车辆制造有限公司,山东 菏泽 274400
摘要:针对采用传统人工势场法进行车辆路径规划时易造成局部极小值与目标不可达的问题,通过改变斥力函数并增加车道边界约束条件函数的方式改进传统人工势场法,进行车辆路径规划。采用模型预测控制(model predictive control,MPC)算法跟踪控制改进人工势场法生成的规划路径,采用软件CarSim与Simulink搭建联合仿真模型对路径跟踪效果进行仿真试验。结果表明:改进人工势场法路径规划合理有效;跟踪路径与规划路径的横向误差小于0.4 m。改进人工势场法和MPC算法应用于无人驾驶车辆的路径规划与跟踪控制具有可行性。
关键词:人工势场法;MPC算法;路径规划;跟踪控制;联合仿真
Vehicle path planning and tracking control based on improved artificial potential field method
HAI Zhenyang1, WANG Jian1*, LI Dasheng2, YANG Zhiyong2,
MU Sikai1, WANG Yunjing1, DENG Huan1
1.School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China;
2.Shandong Lukuo Vehicle Manufacturing Co., Ltd., Heze 274400, China
Abstract:To solve the problem that the local minimum and the target are easily unreachable when the traditional artificial potential field method is used for vehicle path planning, it is improved by changing the repulsive force function and adding the constraint function of lane boundary. A model predictive control (MPC) algorithm is used to track and control the planned path generated by the improved artificial potential field method, and a joint simulation model with software CarSim and Simulink is built to test the path tracking effect. The simulation results show that the improved artificial potential field method is reasonable and effective; the lateral error between tracking path and planning path is less than 0.4 m. It is feasible to apply the improved artificial potential field method and MPC algorithm in the path planning and tracking control of unmanned vehicles.
Keywords:artificial potential field method; MPC algorithm; path planning; tracking control; joint simulation