Chance constrained reachability in environments with spatially varying energy costs

Abstract

In off-road environments, energy costs are highly uncertain and variable due to unknown terrains. To plan missions for robots with limited energy storage capacity, a robot’s reachable set must be determined. This work presents a novel approach for learning reachable sets based on data collected during a mission. Leveraging the authors’ previous work, a spatial energy map of an unknown environment, built with data collected by a robot, can be used to compute a chance constrained reachable set (CCRS) based on a user-defined confidence level. Simulations demonstrate that as a robot collects more data on an energy map, the true positive rate of a computed CCRS improves significantly while the false positive rate remains low, implying that a robot’s reachability can be robustly determined for use in future missions.