COME TOGETHER: Multi-Agent Geometric Consensus (Gathering, Rendezvous, Clustering, Aggregation)

TL;DR

This paper surveys distributed algorithms for mobile agents to achieve geometric consensus, gathering, or clustering, considering various sensing capabilities and motion rules in multi-agent systems.

Contribution

It provides a comprehensive analysis of eight different problems involving sensing ranges, information types, and motion models in multi-agent geometric consensus tasks.

Findings

Different sensing ranges impact convergence properties.

Full vs partial information affects algorithm robustness.

Discrete and continuous motions have distinct convergence behaviors.

Abstract

This report surveys results on distributed systems comprising mobile agents that are identical and anonymous, oblivious and interact solely by adjusting their motion according to the relative location of their neighbours. The agents are assumed capable of sensing the presence of other agents within a given sensing range and able to implement rules of motion based on full or partial information on the geometric constellation of their neighbouring agents. Eight different problems that cover assumptions of finite vs infinite sensing range, direction and distance vs direction only sensing and discrete vs continuous motion, are analyzed in the context of geometric consensus, clustering or gathering tasks.