Target assignment for robots constrained by limited communication range

TL;DR

This paper presents centralized and decentralized algorithms for task assignment in multi-robot systems constrained by limited communication range, optimizing travel time while ensuring connectivity and efficient information sharing.

Contribution

It introduces novel rendezvous-based and TSP-based algorithms that improve task assignment under communication constraints, with analysis of their performance relative to communication range.

Findings

Proposed algorithms outperform greedy methods in simulations.

Performance improves with increased communication range.

Decentralized approach achieves near-centralized efficiency.

Abstract

This paper investigates the task assignment problem for multiple dispersed robots constrained by limited communication range. The robots are initially randomly distributed and need to visit several target locations while minimizing the total travel time. A centralized rendezvous-based algorithm is proposed, under which all the robots first move towards a rendezvous position until communication paths are established between every pair of robots either directly or through intermediate peers, and then one robot is chosen as the leader to make a centralized task assignment for the other robots. Furthermore, we propose a decentralized algorithm based on a single-traveling-salesman tour, which does not require all the robots to be connected through communication. We investigate the variation of the quality of the assignment solutions as the level of information sharing increases and as the communication range grows, respectively. The proposed algorithms are compared with a centralized algorithm with shared global information and a decentralized greedy algorithm respectively. Monte Carlo simulation results show the satisfying performance of the proposed algorithms.