🤖 AI Summary
This work addresses the challenge of decentralized collaborative perception and navigation for multi-robot systems operating in dynamic, unknown environments under low-bandwidth ad-hoc networking constraints. We propose a lightweight distributed framework that leverages an interpretable information-flow mechanism and an environment uncertainty interaction field model to enable observation sharing, complementary uncertainty fusion, and conflict-free convergent/divergent motion. Furthermore, we design a fully decentralized, training-free, self-organizing path optimization algorithm that requires no central coordinator. The approach significantly reduces path redundancy (average 38% reduction in simulation and real-world experiments), enhances task robustness (52% improvement in success rate under communication disruptions), and incurs minimal computational and communication overhead—enabling real-time coordination among ten or more robots. The framework has been validated on edge computing platforms, demonstrating plug-and-play deployment and scalability.
📝 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.