Unmanned Vehicles Path Planning Based on Dynamic Obstacle State Space Reconstruction
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.
Unmanned VehicleDynamic ObstaclesState Space ReconstructionPath Planning
NETL
NSTL
万方数据
