グラフニューラル演算子による異種マルチロボットの分散タスク割り当て
Graph-based Decentralized Task Allocation for Multi-Robot Target Localization
UGVとUAVからなる異種ロボットチームの目標位置推定のため、グラフアテンションを用いた分散型タスク割り当てモデルを提案し、ロボット数やタスクが変化する状況でも高い性能と拡張性を示した。
著者: Juntong Peng, Hrishikesh Viswanath, Aniket Bera
分類: cs.RO, cs.MA
原文アブストラクト
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.
