Dual-Attention Heterogeneous GNN for Multi-robot Collaborative Area Search via Deep Reinforcement Learning

2026-01-07

arXiv.org

AI Summary

This work addresses the challenge of dynamically balancing exploration of unknown regions and coverage of specific targets—such as rescue points—in multi-robot cooperative area search. The authors propose a heterogeneous graph-based deep reinforcement learning approach that constructs a graph encompassing robots, frontier points, and points of interest. To effectively decouple and jointly optimize exploration and coverage objectives, they design a dual attention mechanism that is both relation-aware and type-aware. Experimental results on the iGibson simulation platform using Gibson and MatterPort3D datasets demonstrate that the proposed method significantly outperforms existing approaches in terms of scalability and cross-scenario generalization capability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• multi-robot collaborative area search
• exploration vs. coverage
• heterogeneous graph representation
• dynamic task balancing
• target rescue

Innovation

• Heterogeneous Graph Neural Network
• Dual-Attention Mechanism
• Multi-robot Collaboration
• Deep Reinforcement Learning
• Area Search