An Efficient Sampling and Search Method Based on Risk Framework in Highly Dynamic Scenarios

It is still a challenge to quickly plan a high-quality path that is collision-free and kinematically feasible in a highly dynamic environment with dense dynamic obstacles. Traditional risk-based motion planning algorithms solve the planning problem in the environment of human-machine coexistence by considering the risk of collision with dynamic and static obstacles. Although these algorithms solve the security problems of planning, they have problems such as too many iterations and low quality of planning paths. Therefore, combined with the Artificial Potential Field method (APF), this paper introduces an efficient sampling method into the risk-based motion planning algorithm to guide the growth of random trees to the feasible region, effectively reduce the redundant nodes of random trees, and improve the quality of planning trajectories. Finally, simulation experiments are carried out in different highly dynamic hybrid scenarios, and the effectiveness of the proposed method is verified by comparing with Risk-RRT and Multi-Risk-RRT.