动态障碍物状态空间重构的无人车路径规划

Unmanned Vehicles Path Planning Based on DynamicObstacle State Space Reconstruction

为了保证无人驾驶汽车在动态障碍物的环境下,实现安全的避障路径规划,提出了一种基于动态障碍物状态空间重构的避障路径规划方法。该避障路径规划方法,以前轮偏角为控制量,运用模型预测控制(MPC)方法,忽略车身尺寸信息建立点质量模型,主要在规划层中利用车载传感器的采集信息,对动态障碍物状态空间信息进行重构,研究不同重构构型对避障效果以及“穿越”现象的影响问题。在特定行驶工况中,采用CarSim和Simulink进行联合仿真,以低中高三种速度进行动态障碍物避障稳定性分析,仿真结果表明,所提出的避障路径规划方法对于存在动态障碍物环境下是有效的,避免了由于忽略车身尺寸所导致的“穿越”现象,通过与传统人工势场法比较,验证了本方法规划的路径更短,避障曲率更优。基于动态障碍物状态空间重构的避障路径规划具有安全性和稳定性。

In order to ensure the safe obstacle avoidance path planning for driverless cars in the environment of dynamic obsta-cles,an obstacle avoidance path planning method based on dynamic obstacle state space reconstruction is proposed.This obstacle avoidance path planning method uses the previous wheel deflection angle as the control amount,uses the model predictive control(MPC)method,and ignores the body size information to establish a point quality model.It mainly uses the information collected by on-board sensors in the planning layer to detect the dynamic obstacle status.The spatial information is reconstructed,and the effects of different reconstruction configurations on the obstacle avoidance effect and the“pass-through”phenomenon are studied.Under specific driving conditions,CarSim and Simulink are used for joint simulation,and the obstacle avoidance stability analy-sis of dynamic obstacles is performed at three speeds:low,medium,and high.The simulation results show that the proposed ob-stacle avoidance path planning method is suitable for dynamic obstacle environments.It is effective to avoid the“crossing”phe-nomenon caused by ignoring the body size.By comparing with the traditional artificial potential field method,it is verified that the path planned by this method is shorter and the obstacle avoidance curvature is better.Obstacle avoidance path planning based on dynamic obstacle state space reconstruction is safe and stable.