面向多机器人目标定位的基于图的分布式任务分配
Graph-based Decentralized Task Allocation for Multi-Robot Target Localization
AI总结：
本研究针对异构多机器人系统的目标定位任务，提出基于图注意力的分布式任务分配模型GATR及异构感知预处理技术，可适配2-12台机器人的动态场景，性能优于基准方法。
摘要：
我们提出了一种基于新型图神经算子的任务分配方法，用于由无人地面车辆（Unmanned Ground Vehicles, UGVs）和无人机（Unmanned Aerial Vehicles, UAVs）组成的异构机器人系统。所提出的模型\texttt{\textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R}}（图注意力任务分配器，简称GATR）会聚合多机器人系统中邻居节点的信息，旨在实现全局最优的目标定位。由于采用分布式架构，我们的方法具备极强的鲁棒性，能够适应机器人数量和任务数量随时间变化的场景。我们还提出了一种感知异构性的预处理技术，用于对系统的异构特性进行建模。实验结果表明，在通过改变UGV与UAV的数量、目标的数量及位置生成的一系列模拟场景中，该方法均具备有效性与可扩展性。我们验证了单个模型即可管理2到12台规模的异构机器人团队，且性能优于基准架构。
英文摘要：
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.
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