The Maverick planner: An efficient hierarchical planner for autonomous vehicles in unstructured environments

Abstract

Planning kinodynamically feasible trajectories for autonomous vehicles is computationally expensive, especially when planning over long distances in unstructured environments. This paper presents a hierarchical planner, called the Maverick planner, which can find such trajectories efficiently. It comprises two parts: a waypoint planner that uses a simplified vehicle model and an RRT* planner that respects full kinodynamic constraints. The waypoint planner quickly finds a directed graph of waypoints from start to goal, which is then used to bias sampling and speed up computation in RRT*. The Maverick planner is capable of anytime planning and continuous replanning. It has been tested extensively in simulation and on real vehicles. When planning on a sensor-generated map of the SwRI test track it can find a feasible path over 0.5 km in under 16 ms, and refine that path to within 1% of the local optimum in 0.5 seconds.