Abstract:

This paper introduces a hierarchical motion planning strategy for autonomous tractor-trailer systems, designed for efficient long-horizon, collision-free maneuvering in complex environments. By combining high-level reference line graph search with low-level primal-dual reinforcement learning (RL)- based trajectory optimization, our approach addresses the computational challenges inherent to the motion planning of tractor-trailer dynamics. The high-level graph search decides waypoints guided by Reeds-Shepp cost, and the low-level RL connects the waypoints with dynamically feasible and collision-free trajectories. To enhance safety and accuracy, we incorporate reachability constraints and batch trajectory sampling in the RL algorithm design. Empirical results show that our method significantly reduces computation time, out- performing traditional state-lattice-based planning approaches and enabling real-time applicability.