Abstract: Aiming at the problems of low real-time performance and poor track smoothness of traditional unmanned vehicle tra-jectory planning algorithm in unstructured scenes,this paper proposes a front and rear separated trajectory planning algorithm.The front-end path search part of the algorithm prunes the search range of the Hybrid A^* algorithm in the control space and retains the kinematic constraints of the vehicle,and improves the real-time performance of the graph search by optimizing the calculation method of the heuristic function.The back-end trajectory optimization part of the algorithm is divided into two stages:in the first stage,a soft-constrained nonlinear multi-objective optimizer is designed to locally optimize the path and generate discrete trajectory pose points and time allocation values;in the second stage,based on the quintic spline uses the idea of minimizing Jerk to smoothly connect the discrete pose points,which improves the smoothness of the trajectory.Finally,the proposed algorithm is tested on a real vehicle in an outdoor parking lot environment.Experimental results of front-end path search and back-end trajectory optimization show that the algorithm has high real-time performance and trajectory smoothness.

Key words: Trajectory planning, Unmanned vehicle, Unstructured scenarios, Multi-objective optimization, Splines