Graph-based Decentralized Task Allocation for Multi-Robot Target Localization

TL;DR

This paper presents a graph neural network-based decentralized method for task allocation in heterogeneous multi-robot systems, improving target localization efficiency and scalability in dynamic environments.

Contribution

It introduces exttt{ extbackslash method}, a novel graph attention-based model that is robust, adaptable, and capable of handling varying team sizes and heterogeneity.

Findings

Outperforms baseline architectures in simulated scenarios

Handles 2 to 12 robots effectively

Demonstrates robustness and scalability

Abstract

We introduce a new graph neural operator-based approach for task allocation in a system of heterogeneous robots composed of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs). The proposed model, \texttt{\method}, or \textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R} aggregates information from neighbors in the multi-robot system, with the aim of achieving globally optimal target localization. Being decentralized, our method is highly robust and adaptable to situations where the number of robots and the number of tasks may change over time. We also propose a heterogeneity-aware preprocessing technique to model the heterogeneity of the system. The experimental results demonstrate the effectiveness and scalability of the proposed approach in a range of simulated scenarios generated by varying the number of UGVs and UAVs and the number and location of the targets. We show that a single model can handle a heterogeneous robot team with a number of robots ranging between 2 and 12 while outperforming the baseline architectures.