Abstract
This paper investigates the coverage control problem for multi-agent systems in regions with unknown environmental models. To achieve the coverage control objective, a proper cost function is employed to evaluate the coverage performance of the agent system, and a new type of learning based distributed adaptive coverage control law is proposed to drive the movement of the agents. Local estimation of the unknown environmental model is performed by using the distributed recursive least-squares (D-RLS) method. Accordingly, a novel distributed coverage control strategy is derived to drive the agents to a near-optimal coverage configuration. Each agent only needs to get the local measurement and transmit information with neighboring agents. The convergence of the proposed coverage control law is proven under the condition that the local parameter estimation error converges to zero. Finally, numerical simulation is performed which illustrates that the proposed method is effective and has better performance than previous methods.
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Wang, Y., Tang, M., Fu, J. (2023). Adaptive Coverage Control for Multi-agent Systems in Unknown Environments. In: Ren, Z., Wang, M., Hua, Y. (eds) Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Lecture Notes in Electrical Engineering, vol 934. Springer, Singapore. https://doi.org/10.1007/978-981-19-3998-3_105
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DOI: https://doi.org/10.1007/978-981-19-3998-3_105
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