Risk-aware Integrated Task and Motion Planning for Versatile Snake Robots under Localization Failures

TL;DR

This paper introduces BLISS, a novel integrated task and motion planning approach for snake robots that improves resilience to localization failures and reduces computation time through a convex reformulation of a complex decision process.

Contribution

The paper's main contribution is reformulating a complex hybrid POMDP as a tractable convex MILP, enabling faster and joint task-motion planning for snake robots under localization uncertainties.

Findings

Over an order of magnitude faster computation than state-of-the-art planners.

More than 50% improvement in navigation time optimality.

Successful validation through simulations and hardware experiments.

Abstract

Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots remain an open challenge due to the proximity of the sensor payload to the ground coupled with a limited field of view. To address this issue, we propose Blind-motion with Intermittently Scheduled Scans (BLISS) which combines proprioception-only mobility with intermittent scans to be resilient against both localization failures and collision risks. BLISS is formulated as an integrated Task and Motion Planning (TAMP) problem that leads to a Chance-Constrained Hybrid Partially Observable Markov Decision Process (CC-HPOMDP), known to be computationally intractable due to the curse of history. Our novelty lies in reformulating CC-HPOMDP as a tractable, convex Mixed Integer Linear Program. This allows us to solve BLISS-TAMP significantly faster and jointly derive optimal task-motion plans. Simulations and hardware experiments on the EELS snake robot show our method achieves over an order of magnitude computational improvement compared to state-of-the-art POMDP planners and $>$ 50\% better navigation time optimality versus classical two-stage planners.