Capturing a Moving Target on Star Graphs by Two Communicating Mobile Agents

2026-10-08

AI Summary

This study addresses the problem of two communication-limited agents cooperatively searching for a moving target located far from the origin on a star graph. By considering two communication models under varying knowledge assumptions, the proposed approach integrates online search algorithms, game-theoretic analysis, and distributed cooperative control to design collaborative strategies for two robots that optimize the competitive ratio. The contributions reveal how the number of rays and communication modes influence the competitive ratio, rigorously derive theoretical upper and lower bounds across multiple scenarios, and formally validate the effectiveness of cooperative search.

Abstract

We study a problem of searching for a mobile target in an $m$-ray star graph, a natural generalization of linear search to multiple directions. The target is placed adversarially on one of the rays and may move with constant speed. We investigate a two-robot setting, where cooperation and communication play a central role. We study two communication models: the Face-to-Face (F2F) model, where robots communicate only upon meeting, and the Sender-Receiver (S/R) model, where communication is asymmetric. We focus on the {\em away model}, in which the target moves {\em away} from the origin with speed $v<1$. We design search strategies that minimize the competitive ratio and analyze the problem under three knowledge assumptions: \emph{NoDistance}, \emph{NoSpeed}, and \emph{NoKnowledge}. For each setting, we derive upper bounds on the competitive ratio and for some cases, we derive the lower bound. Our results highlight how the number of rays and the communication model influence the competitive ratio.

Problem

• Star Graphs
• Moving Target Search
• Mobile Agents
• Competitive Ratio
• Communication Models

Innovation

• Star Graphs
• Mobile Target Search
• Competitive Ratio
• Two-Agent Cooperation
• Communication Models