Abstract
This paper addresses the safety-critical defensive guidance of autonomous surface vehicles (ASVs) concerning the interception of multiple attackers to protect a given domain within an obstacle environment. First, an optimal defensive matching method is proposed, which efficiently assigns multiple defensive ASVs to corresponding attackers. Then, an optimal collision-free defensive guidance law is developed to efficiently intercept an attacker in the presence of stochastic measurement noise. By using the proposed safety-critical defensive guidance method, the defensive ASVs are able to intercept the matched attackers with guaranteed safety. Through the stability analysis, the closed-loop system is practical stochastic input-to-state stability. Simulation results substantiate the effectiveness of the proposed safety-critical defensive guidance for the domain protection.
This work was supported by the National Key R &D Program of China under Grant 2022ZD0119902, in part by the National Natural Science Foundation of China under Grants 51979020, 52071044, and in part by the Top-notch Young Talents Program of China under Grant 36261402, and in part by the Liaoning Revitalization Talents Program under Grant XLYC2007188, in part by the Dalian High-level Talents Innovation Support Program under Grant 2022RQ010, in part by the Fundamental Research Funds for the Central Universities 3132023508, in part by the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University.
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Kang, T., Gu, N., Wang, D., Peng, Z., Lv, G. (2024). Safety-Critical Defensive Guidance of Autonomous Surface Vehicles for Domain Protection. In: Jiang, GP., Wang, M., Ren, Z. (eds) Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control. CCSICC 2023. Lecture Notes in Electrical Engineering, vol 1204. Springer, Singapore. https://doi.org/10.1007/978-981-97-3340-8_12
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