SCRAMPPI: Contingency-Constrained Planning using Hamilton-Jacobi Reachability

arXiv:2603.26995v2 Announce Type: replace Abstract: Autonomous robots commonly aim to achieve a nominal objective while minimizing a cost. Without contingency preparation, this can leave them vulnerable to mission failure. This is formalized as a trajectory optimization problem over the nominal cost with a contingency constraint: from each checked state along the nominal plan, an admissible contingency maneuver must exist that reaches a designated recovery target within a prescribed contingency horizon. Existing methods either optimize contingency trajectories alongside the nominal plan or evaluate this constraint through computationally expensive nested sampling-based searches. Instead, we introduce SCRAMPPI, which represents this requirement as a reach-avoid problem and leverages Hamilton--Jacobi (HJ) reachability analysis to evaluate contingency feasibility. By either computing a reach-avoid value function offline or updating it only as the environment is revealed, and integrating it with model predictive path integral (MPPI) control via resampling-based rollouts, SCRAMPPI provides contingency-feasibility evaluations while still planning in 15-20 hz. Finally, we present simulated and hardware experiments demonstrating real-time nominal planning and execution of contingency maneuvers in mobile-robot navigation, alongside a five-dimensional friction-limited recovery simulation.

Abstract:Autonomous robots commonly aim to achieve a nominal objective while minimizing a cost. Without contingency preparation, this can leave them vulnerable to mission failure. This is formalized as a trajectory optimization problem over the nominal cost with a contingency constraint: from each checked state along the nominal plan, an admissible contingency maneuver must exist that reaches a designated recovery target within a prescribed contingency horizon. Existing methods either optimize contingency trajectories alongside the nominal plan or evaluate this constraint through computationally expensive nested sampling-based searches. Instead, we introduce SCRAMPPI, which represents this requirement as a reach-avoid problem and leverages Hamilton--Jacobi (HJ) reachability analysis to evaluate contingency feasibility. By either computing a reach-avoid value function offline or updating it only as the environment is revealed, and integrating it with model predictive path integral (MPPI) control via resampling-based rollouts, SCRAMPPI provides contingency-feasibility evaluations while still planning in 15-20 hz. Finally, we present simulated and hardware experiments demonstrating real-time nominal planning and execution of contingency maneuvers in mobile-robot navigation, alongside a five-dimensional friction-limited recovery simulation.

Robotics (cs.RO)

arXiv:2603.26995 [cs.RO]

(or arXiv:2603.26995v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2603.26995 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

analiza aiqbench

SCRAMPPI: Contingency-Constrained Planning using Hamilton-Jacobi Reachability

Subiecte conexe