Nav-SCOPE: Swarm Robot Cooperative Perception and Coordinated Navigation

Abstract

This paper proposes a lightweight systematic solution for multi-robot coordinated navigation with decentralized cooperative perception. An information flow is first created to facilitate real-time observation sharing over unreliable ad-hoc networks. Then, the environmental uncertainties of each robot are reduced by interaction fields that deliver complementary information. Finally, path optimization is achieved, enabling self-organized coordination with effective convergence, divergence, and collision avoidance. Our method is fully interpretable and ready for deployment without gaps. Comprehensive simulations and real-world experiments demonstrate reduced path redundancy, robust performance across various tasks, and minimal demands on computation and communication.

11 pages, 9 figures, accepted in IEEE Transactions on Automation Science and Engineering

References in corpus (4)

• Multi-Robot Collaborative Perception with Graph Neural Networks
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• Collaborative Goal Tracking of Multiple Mobile Robots Based on Geometric Graph Neural Network
• Highly Efficient Observation Process based on FFT Filtering for Robot Swarm Collaborative Navigation in Unknown Environments