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arXiv · 2409.11561

Hypergraph-based Coordinated Task Allocation and Socially-aware Navigation for Multi-Robot Systems

Weizheng Wang·Aniket Bera·Byung-Cheol Min
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Abstract

A team of multiple robots seamlessly and safely working in human-filled public environments requires adaptive task allocation and socially-aware navigation that account for dynamic human behavior. Current approaches struggle with highly dynamic pedestrian movement and the need for flexible task allocation. We propose Hyper-SAMARL, a hypergraph-based system for multi-robot task allocation and socially-aware navigation, leveraging multi-agent reinforcement learning (MARL). Hyper-SAMARL models the environmental dynamics between robots, humans, and points of interest (POIs) using a hypergraph, enabling adaptive task assignment and socially-compliant navigation through a hypergraph diffusion mechanism. Our framework, trained with MARL, effectively captures interactions between robots and humans, adapting tasks based on real-time changes in human activity. Experimental results demonstrate that Hyper-SAMARL outperforms baseline models in terms of social navigation, task completion efficiency, and adaptability in various simulated scenarios.

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Weizheng Wang, Aniket Bera, Byung-Cheol Min. 2024-09-17. Hypergraph-based Coordinated Task Allocation and Socially-aware Navigation for Multi-Robot Systems. https://arxiv.org/abs/2409.11561

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