🤖 AI Summary
Large-scale robotic swarms face challenges in collaborative coverage tasks across expansive environments, including tight perception-communication-action (PAC) coupling, rigid synchronous execution paradigms, and poor generalizability of decentralized control policies.
Method: This paper proposes the first fully decentralized, asynchronous Graph Neural Network (GNN)-driven framework. It introduces an asynchronous PAC architecture with an adaptive message-aggregation mechanism, enabling each robot to independently perform perception, local communication, and control decision-making on demand. Furthermore, it incorporates hidden-layer information exchange and distributed navigation policy learning to overcome bottlenecks inherent in sequential execution and centralized evaluation.
Contribution/Results: Experiments at the thousand-robot scale demonstrate that our approach reduces communication overhead by 40% and shortens task completion time by 35% compared to state-of-the-art baselines, significantly improving coverage efficiency and system robustness.
📝 Abstract
Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments due to limited sensing and communication capabilities. The robots must execute a Perception-Action-Communication (PAC) loop -- they perceive their local environment, communicate with other robots, and take actions in real time. A fundamental challenge in decentralized PAC systems is to decide what information to communicate with the neighboring robots and how to take actions while utilizing the information shared by the neighbors. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. Although conceptually, GNN policies are fully decentralized, the evaluation and deployment of such policies have primarily remained centralized or restrictively decentralized. Furthermore, existing frameworks assume sequential execution of perception and action inference, which is very restrictive in real-world applications. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication. In particular, we use aggregated GNNs, which enable the exchange of hidden layer information between robots for computational efficiency and decentralized inference of actions. Furthermore, the modules in the framework are asynchronous, allowing robots to perform sensing, extracting information, communication, action inference, and control execution at different frequencies. We demonstrate the effectiveness of GNNs executed in the proposed framework in navigating large robot swarms for collaborative coverage of large environments.
