An Efficient Global Trajectory Planner for Highly Dynamical Nonholonomic Autonomous Vehicles on 3D Terrains

A novel hierarchical global trajectory planner is presented to allow highly dynamical nonholonomic off-road autonomous vehicles to achieve high mobility on 3D terrains. On complex terrains with uneven topology, designing safe and feasible vehicle trajectories often demands an understanding of the vehicle's dynamical and nonholonomic constraints. Prior research, however, treats the global planning problem as a path planning problem without effectively accounting for topology or dynamical constraints. To address this gap, this paper presents a three-phase trajectory planning algorithm composed of an A*, a rapidly exploring random tree (RRT), and a local trajectory refining (LTR) phase to incorporate dynamical and nonholonomic constraints on uneven terrain. The algorithm is tested in scenarios with randomized terrain fields and obstacles to demonstrate the necessity for all three phases. The algorithm is shown to have lower cost, higher success rate, and higher computational efficiency compared to state-of-the-art methods. The algorithm is then tested by controlling a simulated MRZR vehicle on a 3D terrain along with a local controller, with comparisons to state-of-the-art algorithms. It is demonstrated that the new algorithm is capable of planning dynamically feasible trajectories with lower cost where the state-of-the-art algorithms fail to perform due to neglecting dynamical vehicle limitations.