Potential Field Exploring Tree Path Planning for Intelligent Vehicle in Off-road Environment
Path planning is a key technology for intelligent vehicles.The traditional vehicle path planning method takes the shortest traveling distance or minimum traveling time as the optimization goal,ignoring the risk of vehicle motion.A potential field based rapidly-exploring random tree(RRT)algorithm is proposed.The potential field model is used to quantitatively evaluate the driving risk,and a low-risk initial vehicle driving trajectory is obtained efficiently using the RRT algorithm.And then a trajectory reconstruction optimization method is adopted to continuously optimize the vehicle driving trajectory based on the driving safety,traveling distance and turning angle.The scenario simulation is used to verify the performance of the planning solution.The simulated results show that the proposed algorithm can balance the path planning efficiency and safety performance,while avoiding the obstacles and environmental threats in off-road environment.The planned trajectories conform to the vehicle kinematics features,good safety and high traveling efficiency.
intelligent vehicleoff-road environmentpotential field modelrisk evaluationrapidly-exploring random treepath planning
NETL
NSTL
万方数据
