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This research establishes a formal equivalence between complementarity constraints and control barrier functions, creating a unified theoretical framework for safe whole-body robot control. By bridging these two methodologies, the study enables cross-application of robustness guarantees and algorithmic improvements critical for real-time collision avoidance in safety-critical systems. The work advances foundational robotics theory rather than focusing on consumer AI applications or quantum computing advancements.
Unifying Complementarity Constraints and Control Barrier Functions for Safe Whole-Body Robot Control
arXiv:2504.17647v2 Announce Type: replace Abstract: Safety-critical whole-body robot control demands reactive methods that ensure collision avoidance in real-time. Complementarity constraints and control barrier functions (CBF) have emerged as core tools for ensuring such safety constraints, and each represents a well-developed field. Despite addressing similar problems, their connection remains largely unexplored. This paper bridges this gap by formally proving the equivalence between these two methodologies for sampled-data, first-order systems, considering both single and multiple constraint scenarios. By demonstrating this equivalence, we provide a unified perspective on these techniques. This unification has theoretical and practical implications, facilitating the cross-application of robustness guarantees and algorithmic improvements between complementarity and CBF frameworks. We discuss these synergistic benefits and motivate future work in the comparison of the methods in more general cases.
Abstract:Safety-critical whole-body robot control demands reactive methods that ensure collision avoidance in real-time. Complementarity constraints and control barrier functions (CBF) have emerged as core tools for ensuring such safety constraints, and each represents a well-developed field. Despite addressing similar problems, their connection remains largely unexplored. This paper bridges this gap by formally proving the equivalence between these two methodologies for sampled-data, first-order systems, considering both single and multiple constraint scenarios. By demonstrating this equivalence, we provide a unified perspective on these techniques. This unification has theoretical and practical implications, facilitating the cross-application of robustness guarantees and algorithmic improvements between complementarity and CBF frameworks. We discuss these synergistic benefits and motivate future work in the comparison of the methods in more general cases.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2504.17647 [cs.RO]
(or arXiv:2504.17647v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2504.17647 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/TAC.2026.3716907 Focus to learn more DOI(s) linking to related resources
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