Disentangled Control of Multi-Agent Systems
Lin, Ruoyu, Notomista, Gennaro, Egerstedt, Magnus
–arXiv.org Artificial Intelligence
This paper develops a general framework for multi-agent control synthesis, which applies to a wide range of problems with convergence guarantees, regardless of the complexity of the underlying graph topology and the explicit time dependence of the objective function. The proposed framework systematically addresses a particularly challenging problem in multi-agent systems, i.e., decentralization of entangled dynamics among different agents, and it naturally supports multi-objective robotics and real-time implementations. To demonstrate its generality and effectiveness, the framework is implemented across three experiments, namely time-varying leader-follower formation control, decentralized coverage control for time-varying density functions without any approximations, which is a long-standing open problem, and safe formation navigation in dense environments.
arXiv.org Artificial Intelligence
Nov-26-2025
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