Learning-informed Long-Horizon Navigation under Uncertainty for Vehicles with Dynamics

Abstract

We present a novel approach to learning-augmented, long-horizon navigation under uncertainty in large-scale environments in which considering the robot dynamics is essential for informing good behavior. Our approach tightly integrates sampling-based motion planning, which computes dynamically feasible routes to the goal through different unexplored boundaries, and a high-level planner that leverages predictions about unseen space to select a route that best makes progress toward the unseen goal. Owing to its ability to understand the impacts of the robot’s dynamics on how it should attempt to reach the goal, our approach achieves both higher reliability and improved navigation performance compared to competitive learning-informed and non-learned baselines in simulated office-building-like environments.