DAS-MPPI: Disturbance-Aware Safe MPPI Planner With Conservatism-Reducing Reachability Filtering

Abstract

Safe motion planning in cluttered environments under unknown disturbances remains a major challenge for robotic systems. Model predictive path integral (MPPI) control is attractive for non-convex planning, yet its penalty-based formulation cannot provide deterministic safety guarantees. This paper presents Disturbance-Aware Safe MPPI (DAS-MPPI), a robust sampling-based framework that couples MPPI with Hamilton–Jacobi reachability to enforce reachability-certified safety under unknown disturbances. Unlike prior methods that assume fixed worst-case disturbance bounds, DAS-MPPI formulates the safety game against a convergent disturbance-estimation error set. This dynamically shrinks the backward reachable tube (BRT), reducing conservatism and expanding the navigable free space. Furthermore, we embed this adaptive BRT into MPPI as both a rollout filter and a final control filter, while using online disturbance estimates to compensate for lumped disturbances and mitigate steady-state tracking errors. Extensive comparative simulations show that DAS-MPPI substantially reduces unnecessary safety interventions compared with state-of-the-art baselines. Under moderate and time-varying disturbances, it improves both planning efficiency and success rate, while under the prescribed worst-case disturbance bound it maintains robust collision-avoidance performance. Hardware experiments on a quadruped robot further demonstrate its practical applicability in real-world scenarios. Note to Practitioners—This paper is motivated by the challenge of deploying autonomous mobile robots in cluttered environments under unknown disturbances. For instance, autonomous transport robots operating in dense warehouse settings are routinely affected by uneven ground surfaces, wheel-ground interaction changes, and varying payloads. Conventional approaches typically rely on conservative, static worst-case disturbance bounds, which can force robots into overly cautious, inefficient maneuvers while maintaining collision avoidance. To address this limitation, this paper proposes a general robust motion planning framework that estimates external disturbances in real time and dynamically adjusts safety margins accordingly. This adaptive capability enables robots to traverse tight spaces more efficiently while compensating for drift to achieve safe and accurate trajectory tracking. Overall, the framework provides practitioners with a practical way to reduce the conservatism of worst-case safety margins. Validated through simulations and hardware experiments, the proposed method reduces conservative behaviors and improves planning efficiency under moderate and time-varying disturbances while retaining reachability-based safety filtering.