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January 17, 2026Applied SciencesOpen Access

Multi-Robot Task Allocation with Spatiotemporal Constraints via Edge-Enhanced Attention Networks

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Authors

YHYu HuDLDaxue LiuJLJinhong Li

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Overview

Research demonstrates improved task success rates in multi-robot systems with complex spatial constraints, indicating enhanced adaptability.

Key Points

  • The aim is to improve multi-robot task allocation while considering spatiotemporal constraints and environmental adaptability.
  • Formulated the problem as an asynchronous Markov Decision Process over a directed heterogeneous graph.
  • Developed a novel Edge-Enhanced Attention Network (E2AN) to address spatial information distortion.
  • Integrated an Edge-Enhanced Heterogeneous Graph Attention Network (E2HGAT) with attention mechanisms for decoding tasks.
  • Conducted extensive experiments in simulated environments with obstructions to evaluate performance.
  • The proposed method significantly outperformed baseline algorithms in task success rate.
  • Maintained advantages in generalization tests on unseen maps and scalability across problem sizes.
  • Ablation studies confirmed the encoder's importance in capturing spatiotemporal dependencies.
  • Real-time performance analysis validated feasibility for online deployment.

Cite This Study

Hu et al. (2026) studied this question.

synapsesocial.com/papers/696b2696d2a12237a9349db5https://doi.org/10.3390/app16020904
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