Nav-SCOPE: Swarm Robot Cooperative Perception and Coordinated Navigation

This paper proposes a lightweight decentralized solution for multi-robot coordinated navigation with cooperative perception. First, we introduce a rapid way to process sensory data, thus obtaining safe directions and key environmental features. Then, an information flow is created to facilitate real-time perception sharing over wireless ad-hoc networks. Consequently, the environmental uncertainties of each robot are reduced by interaction fields that deliver complementary information. Finally, path optimization is achieved in a probabilistic way, 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. Note to Practitioners—Local perception is the information source for robot navigation in unknown environments. The extended perception at the swarm level provides complementary information for each robot. This can optimize robot paths and achieve coordinated navigation in a distributed way. However, this purpose is currently unachievable due to the high demands of communication and computation on board. This study reduces these two demands to the extreme by Fast Fourier Transform (FFT), digital filtering, probabilistic fusion, and swarm interaction forces. At the same time, our fully interpretable method avoids sim-to-real gaps and can be deployed directly on ground mobile robots. Simulation and real-world experiments outperform a state-of-the-art approach. Future work aims to extend our method to aerial robots as drones.