Distribution-Transfer Safe-Horizon MPC under Mode Uncertainty

Abstract:Scenario-based MPC is an attractive strategy for chance-constrained motion planning that approximates uncertainty via a finite set of sampled scenarios. As a sampling-based method, scenario-based MPC is sensitive to distribution mismatch. We address this problem in the context of Safe-Horizon Model Predictive Control (SH-MPC) with obstacles governed by switching dynamic modes. From finite mode observations, we construct a confidence set for the unknown categorical mode law and derive a multiplicative domination bound that transfers a Safe-Horizon collision-risk certificate from a selected scenario-sampling distribution to every law in the confidence set. Wasserstein geometry is used to regularize probability reallocation among modes according to the similarity of their induced trajectory predictions, while a collision-risk surrogate biases sampling toward dangerous modes. The resulting certificate explicitly quantifies the additional tightening required under distribution mismatch and exposes the multiplicative conservatism that arises when several obstacle-wise transfer factors are combined

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2610.07277 [cs.RO]

(or arXiv:2610.07277v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.07277 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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