The Multi-Agent Rendezvous Problem. Part 2: The Asynchronous Case

By Ji Lin, A. Stephen Morse, B.D.O. Anderson. 2007

217 citations recorded by the source corpus.

This paper is concerned with the collective behavior of a group of $n>1$ mobile autonomous agents, labelled $1$ through $n$, which can all move in the plane. Each agent is able to continuously track the positions of all other agents currently within its “sensing region,” where by an agent's sensing region we mean a closed disk of positive radius $r$ centered at the agent's current position. The multi-agent rendezvous problem is to devise “local” control strategies, one for each agent, which without any active communication between agents cause all members of the group to eventually rendezvous at a single unspecified location. This paper describes a family of unsynchronized strategies for solving the problem. Correctness is established appealing to the concept of “analytic synchronization.”

Subfields: Computer Networks and Communications, Mechanical Engineering

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In Computer Networks and Communications

In Mechanical Engineering

Citations in the corpus

Deep Reinforcement Learning for Swarm Systems (2018, background): “Optimization–based approaches can compute optimal control policies for tasks that can be well modeled, such as rendezvous or consensus problems [Lin et al., 2007] and formation control [Ranjbar-Sahraei et al., 2012], or to learn pursuit strategies to capture an evader [Zhou et al., 2016].”
State Agreement for Continuous-Time Coupled Nonlinear Systems (2007, methodology): “These algorithms were extended to various synchronous and asynchronous stop-andgo strategies in [9, 19, 20].”
Distributed Control of Robotic Networks (2009, background): “(1984) Linear System Theory and Design, Holt, Rinehart, and Winston, ISBN 0030602890. Chopra, N. and Spong, M. W. (2009) On exponential synchronization of Kuramoto oscillators, IEEE Transactions on Automatic Control, 54(2), 353–357.”
Reaching a Consensus in a Dynamically Changing Environment: Convergence Rates, Measurement Delays, and Asynchronous Events (2008, methodology): “This is accomplished using the concept of analytic synchronization outlined previously in [9, 10].”
Distributed algorithms for reaching consensus on general functions (2008, background): “Coordination problems that focus, instead, on “spatial versions” of consensus include rendezvous [1, 9, 18 , 19], flocking [4, 14, 21, 22], and synchronization [15, 29].”
Agreeing Asynchronously (2008, methodology): “The examples provided by this paper and by [19] and [20] may help to more precisely formulate these issues and to lead to their resolution.”