Contingency Constrained Planning with MPPI within MPPI

TL;DR

This paper introduces Contingency-MPPI, a real-time, data-driven planning method that integrates contingency considerations into autonomous robot planning, improving safety and efficiency in unpredictable environments.

Contribution

It presents a novel approach that embeds contingency planning within MPPI using adaptive importance sampling and initializations from lightweight planners.

Findings

Successfully generates real-time contingency plans on hardware

Improves sampling efficiency with adaptive importance sampling

Demonstrates safety and effectiveness in simulated and real-world tests

Abstract

For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance between nominal and contingency behavior, or plan for a singular contingency plan; however, this does not guarantee that the resulting plan is safe for all time. To address this research gap, this paper presents Contingency-MPPI, a data-driven optimization-based strategy that embeds contingency planning inside a nominal planner. By learning to approximate the optimal contingency-constrained control sequence with adaptive importance sampling, the proposed method's sampling efficiency is further improved with initializations from a lightweight path planner and trajectory optimizer. Finally, we present simulated and hardware experiments demonstrating our algorithm generating nominal and contingency plans in real time on a mobile robot.