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Adaptive Coverage Algorithms for Mobile Ad Hoc Networks in Dynamic Worlds
arXiv:2610.41936v1 Announce Type: new Abstract: This paper extends an existing framework for the coordination of mobile agents in unknown worlds, by considering the possibility of dynamic instability. In the original problem, known as the Partially Observable Cooperative Guard Art Gallery Problem (POCGAGP) (arXiv:2512.09495), the multi-agent system must establish a communication network which completely covers an initially unknown constrained space. However, the POCGAGP fails to account for the unpredictability often seen in real-world deployment. To address this, the dynamic case (DPOCGAGP) introduces two settings which can influence the mobile network during deployment: Agent Loss and World Change, which represent dynamic changes to the world. Within this study, we present advancements to two state-of-the-art algorithms, the Centralized Agent-Directed Exploration for Network Coverage (CADENCE), and its decentralized analogue, DADENCE, to provide solutions in these cases. We then compare their performance against another state-of-the-art benchmark algorithm, and show that the adapted algorithms outperform across our benchmark test set, which comprises over 4500 test cases of varied size and structure.
Abstract:This paper extends an existing framework for the coordination of mobile agents in unknown worlds, by considering the possibility of dynamic instability. In the original problem, known as the Partially Observable Cooperative Guard Art Gallery Problem (POCGAGP) (arXiv:2512.09495), the multi-agent system must establish a communication network which completely covers an initially unknown constrained space. However, the POCGAGP fails to account for the unpredictability often seen in real-world deployment. To address this, the dynamic case (DPOCGAGP) introduces two settings which can influence the mobile network during deployment: Agent Loss and World Change, which represent dynamic changes to the world. Within this study, we present advancements to two state-of-the-art algorithms, the Centralized Agent-Directed Exploration for Network Coverage (CADENCE), and its decentralized analogue, DADENCE, to provide solutions in these cases. We then compare their performance against another state-of-the-art benchmark algorithm, and show that the adapted algorithms outperform across our benchmark test set, which comprises over 4500 test cases of varied size and structure.
8 pages, 10 figures. Accepted to the 2026 IEEE MIT Undergraduate Research Technology Conference (URTC)
Robotics (cs.RO); Multiagent Systems (cs.MA)
arXiv:2610.41936 [cs.RO]
(or arXiv:2610.41936v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.41936 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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