Control of Multi-Agent Systems with Finite Time Control Barrier Certificates and Temporal Logic
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Abstract (arXiv)
In this paper, a method to synthesize controllers using finite time convergence control barrier functions guided by linear temporal logic specifications for continuous time multi-agent dynamical systems is proposed. Finite time convergence to a desired set in the state space is guaranteed under the existence of a suitable finite time convergence control barrier function. In addition, these barrier functions also guarantee forward invariance once the system converges to the desired set. This allows us to formulate a theoretical framework which synthesizes controllers for the multi-agent system. These properties also enable us to solve the reachability problem in continuous time by formulating a theorem on the composition of multiple finite time convergence control barrier functions. This approach is more flexible than existing methods and also allows for a greater set of feasible control laws. Linear temporal logic is used to specify complex task specifications that need to be satisfied by the multi-agent system. With this solution methodology, a control law is synthesized that satisfies the given temporal logic task specification. Robotic experiments are provided which were performed on the Robotarium multi-robot testbed at Georgia Tech.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 0; 2025: 1; 2026: 1.
6 papers.
- 2025SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functionscited by 11
- 2023Online Control Barrier Functions for Decentralized Multi-Agent Navigationcited by 10
- 2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidancecited by 8
- 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learningcited by 3
- 2021Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teamscited by 1
- 2026Robust Taylor-Lagrange Control for Safety-Critical Systemscited by 0
- 2026Robust Taylor-Lagrange Control for Safety-Critical Systems23 Feb 2026
- 2025SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions29 Sep 2025
- 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning31 Oct 2023
- 2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance6 Jul 2023
- 2023Online Control Barrier Functions for Decentralized Multi-Agent Navigation8 Mar 2023
- 2021Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams21 Sep 2021