A Learning-based Approach to Motion Planning with State Lattices in Off-Road Environments

To safely navigate unmanned ground vehicles operating in unstructured and partially observed environments, intelligence architectures require efficient motion planning algorithms that generate near-optimal routes and satisfy motion constraints in real-time. The recombinant nature of state lattice-based search spaces enables efficient search in motion planning graphs with precomputed trajectory libraries that satisfy nonholonomic constraints. The implementation of lattice planner-based search spaces however requires design choices that include resolution, fidelity, and expressiveness of the graph. Parameters that are well tuned for some environments may prove suboptimal or ineffective in others. In this paper we proposed a classification-based approach to parameter learning that optimizes state lattice planner performance online using context from the environment and planning problem definition. Experimental results using data collected from a high-speed off-road unmanned ground vehicle operating in off-road environments demonstrate a substantial improvement in relative optimality of generated trajectories for regional motion planning.