Hierarchical adaptive planning in environments with uncertain, spatially-varying disturbance forces

This paper presents a hierarchical planning architecture that generates vehicle trajectories that adapt to uncertain, spatially-varying disturbance forces toward enhanced tracking performance. The disturbance force is modeled as a discrete conditional probability distribution that is updated online by local measurements as the vehicle navigates. A global planner identifies the optimal route to the goal and adapts this route according to a cost metric derived from the belief distribution on the disturbance force. A local planner embeds the belief distribution in the trajectory generation process to compute dynamically feasible trajectories along the global plan that evolve with the belief. Simulation studies analyze and demonstrate the increased trajectory tracking accuracy via the proposed methodology with a single vehicle and the impact of the approach to multiple agents performing collaborative inference toward enhanced collective performance.