Communication-Free Distributed Multi-Robot Task Allocation under Partial Observations Using Labeled Multi-Bernoulli Filtering

2026-09-30

AI Summary

This study addresses the challenging problem of multi-robot task allocation under partial observability and in the absence of explicit communication. To this end, it proposes a fully decentralized coordination framework in which each robot independently estimates the states of its neighbors from local observations using a Labeled Multi-Bernoulli (LMB) filter, and subsequently solves and dynamically adjusts task assignments via a greedy auction algorithm. The primary contribution lies in achieving implicit multi-robot coordination without any communication overhead. Monte Carlo simulations demonstrate that the proposed approach exhibits strong robustness against measurement clutter and environmental uncertainty, effectively solving collaborative task allocation under constrained conditions.

Abstract

This paper proposes a communication-free multi-robot task allocation framework based solely on local observations. In this study, tasks are defined as reaching target locations. Each robot estimates the positions of neighboring robots using a Labeled Multi-Bernoulli (LMB) filter and independently assigns tasks through a greedy auction-based strategy. By continuously updating state estimates and reallocating tasks during execution, the proposed method enables decentralized coordination without explicit communication. Monte Carlo simulations demonstrate that the proposed method enables effective cooperative task allocation without inter-robot communication while remaining robust to measurement clutter and observation uncertainty.

Problem

• Multi-Robot Task Allocation
• Communication-Free
• Distributed Coordination
• Partial Observations

Innovation

• Communication-Free
• Multi-Robot Task Allocation
• Labeled Multi-Bernoulli Filter
• Greedy Auction
• Partial Observations