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Safety-Aware Pursuit-Evasion Game Based on Control Barrier Function and Reinforcement Learning
Tongji University, College of Electronic and Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China, 200092.ORCID iD: 0000-0002-6319-7627
Tongji University, College of Electronic and Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China, 200092.
Tongji University, College of Electronic and Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China, 200092.ORCID iD: 0000-0002-1687-535X
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, Optimization and Systems Theory.ORCID iD: 0000-0003-0177-1993
2025 (English)In: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, E-ISSN 2168-2232, Vol. 55, no 8, p. 5440-5450Article in journal (Refereed) Published
Abstract [en]

This article considers the pursuit-evasion game of two dynamic systems, which are subject to safety constraints, and in order to additionally guarantee the safety of the system, we propose safety-aware pursuit and escape strategies by combining control barrier function (CBF) and off-policy learning technique. Different from existing pursuit and evader strategies, a safeguarding control law is first designed based on CBF to prioritize the safety of pursuer's and evader's trajectories, and then bounded game strategies are proposed by elaborately designing a new cost function. We also provide the sufficient condition for the stability of the closed-loop system with the state denoted by position difference, under which, the pursuer is able to capture the evader. It is worth mentioning that our strategies do not require the knowledge of system dynamics, which are essentially online learning-based ones, featured with the ability of satisfying the safety constraints in the pursuit-evasion game.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2025. Vol. 55, no 8, p. 5440-5450
Keywords [en]
Control barrier function (CBF), linear system, pursuit-evasion game, reinforcement learning
National Category
Control Engineering Robotics and automation
Identifiers
URN: urn:nbn:se:kth:diva-366568DOI: 10.1109/TSMC.2025.3546968ISI: 001508151200001Scopus ID: 2-s2.0-105008138355OAI: oai:DiVA.org:kth-366568DiVA, id: diva2:1983282
Note

QC 20260120

Available from: 2025-07-10 Created: 2025-07-10 Last updated: 2026-01-20Bibliographically approved

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Hu, Xiaoming

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