In multi-robot collaborative area search, a key challenge is to dynamically balance the two objectives of exploring unknown areas and covering specific targets to be rescued. Existing methods are often constrained by homogeneous graph representations, thus failing to model and balance these distinct tasks. To address this problem, we propose a Dual-Attention Heterogeneous Graph Neural Network (DA-HGNN) trained using deep reinforcement learning. Our method constructs a heterogeneous graph that incorporates three entity types: robot nodes, frontier nodes, and interesting nodes, as well as their historical states. The dual-attention mechanism comprises the relational-aware attention and type-aware attention operations. The relational-aware attention captures the complex spatio-temporal relationships among robots and candidate goals. Building on this relational-aware heterogeneous graph, the type-aware attention separately computes the relevance between robots and each goal type (frontiers vs. points of interest), thereby decoupling the exploration and coverage from the unified tasks. Extensive experiments conducted in interactive 3D scenarios within the iGibson simulator, leveraging the Gibson and MatterPort3D datasets, validate the superior scalability and generalization capability of the proposed approach.

翻译：在多机器人协作区域搜索中，一个关键挑战在于如何动态平衡探索未知区域与覆盖待救援特定目标这两个目标。现有方法通常受限于同构图表示，因而难以对这些不同的任务进行建模与平衡。为解决此问题，我们提出了一种利用深度强化学习训练的双注意力异构图神经网络（DA-HGNN）。我们的方法构建了一个包含三种实体类型（机器人节点、前沿节点和兴趣节点）及其历史状态的异构图。双注意力机制由关系感知注意力和类型感知注意力操作构成。关系感知注意力捕捉了机器人与候选目标之间复杂的时空关系。在此关系感知异构图的基础上，类型感知注意力分别计算机器人与每种目标类型（前沿点与兴趣点）之间的相关性，从而将探索与覆盖任务从统一任务中解耦出来。在iGibson模拟器中，利用Gibson和MatterPort3D数据集，在交互式3D场景中进行的大量实验验证了所提方法具有优异的可扩展性和泛化能力。