Multi-robot task allocation works without communication using local sensing
Communication-Free Distributed Multi-Robot Task Allocation under Partial Observations Using Labeled Multi-Bernoulli Filtering
Robotics
Summary
Assigning tasks to multiple robots usually needs them to talk to each other, but this paper shows a way for robots to work together without any direct communication. Each robot uses local observations to estimate where others are and then decides which target location it should reach by itself. The method keeps updating these decisions as robots move, making coordination happen just from sensing alone. Tests in simulations show this approach works well even when observations are uncertain or cluttered.
What this means in practice
- •For robotic system engineers: Develop multi-robot systems that coordinate tasks effectively without requiring communication infrastructure.
- •For autonomous vehicle developers: Deploy fleets of vehicles that assign destinations independently based on local sensing to overcome communication limitations.
Authors
Takumi Ito, Akiya Kamimura
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.