Robust Barrier Functions for a Fully Autonomous, Remotely Accessible Swarm-Robotics Testbed
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Abstract (arXiv)
The Robotarium, a remotely accessible swarm-robotics testbed, has provided free, open access to robotics and controls research for hundreds of users in thousands of experiments. This high level of usage requires autonomy in the system, which mainly corresponds to constraint satisfaction in the context of users' submissions. In other words, in case that the users' inputs to the robots may lead to collisions, these inputs must be altered to avoid these collisions automatically. However, these alterations must be minimal so as to preserve the users' objective in the experiment. Toward this end, the system has utilized barrier functions, which admit a minimally invasive controller-synthesis procedure. However, barrier functions are yet to be robustified with respect to unmodeled disturbances (e.g., wheel slip or packet loss) in a manner conducive to real-time synthesis. As such, this paper formulates robust barrier functions for a general class of disturbed control-affine systems that, in turn, is key for the Robotarium to operate fully autonomously (i.e., without human supervision). Experimental results showcase the effectiveness of this robust formulation in a long-term experiment in the Robotarium.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 59
- 2024Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equationscited by 3
- 2025JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutescited by 1
- 2026Information-Guided Safe Reinforcement Learning for Autonomous Gas Source Localization using sUAScited by 0
- 2026Information-Guided Safe Reinforcement Learning for Autonomous Gas Source Localization using sUAS4 Sep 2026
- 2025JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes10 May 2025
- 2024Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations23 Jan 2024
- 2021Safe Reinforcement Learning Using Robust Control Barrier Functions11 Oct 2021