Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions
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Abstract (arXiv)
This paper presents an approach to deal with safety of dynamical systems in presence of multiple non-convex unsafe sets. While optimal control and model predictive control strategies can be employed in these scenarios, they suffer from high computational complexity in case of general nonlinear systems. Leveraging control barrier functions, on the other hand, results in computationally efficient control algorithms. Nevertheless, when safety guarantees have to be enforced alongside stability objectives, undesired asymptotically stable equilibrium points have been shown to arise. We propose a computationally efficient optimization-based approach which allows us to ensure safety of dynamical systems without introducing undesired equilibria even in presence of multiple non-convex unsafe sets. The developed control algorithm is showcased in simulation and in a real robot navigation application.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jun 2021.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1.
3 papers.
- 2022Constrained Dynamic Movement Primitives for Safe Learning of Motor Skillscited by 2
- 2022Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learningcited by 1
- 2024TRUST-UP: Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuitcited by 0
- 2024TRUST-UP: Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuit26 Nov 2024
- 2022Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills28 Sep 2022
- 2022Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning17 Jul 2022