The Maverick planner: An efficient hierarchical planner for autonomous vehicles in unstructured environments
Neal Seegmiller, Jason Gassaway, Elliot R. Johnson, Jerry Towler · 2017
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.