Learning Models for Predictive Adaptation in State Lattices

Approaches to autonomous navigation for unmanned ground vehicles rely on motion planning algorithms that optimize maneuvers under kinematic and environmental constraints. Algorithms that combine heuristic search with local optimization are well suited to domains where solution optimality is favored over speed and memory resources are limited as they often improve the optimality of solutions without increasing the sampling density. To address the runtime performance limitations of such algorithms, this paper introduces Predictively Adapted State Lattices, an extension of recombinant motion planning search space construction that adapts the representation by selecting regions to optimize using a learned model trained to predict the expected improvement. The model aids in prioritizing computations that optimize regions where significant improvement is anticipated. We evaluate the performance of the proposed method through statistical and qualitative comparisons to alternative State Lattice approaches for a simulated mobile robot with nonholonomic constraints. Results demonstrate an advance in the ability of recombinant motion planning search spaces to improve relative optimality at reduced runtime in varyingly complex environments.