AdaSharing: Adaptive Data Sharing in Collaborative Robots

Abstract

Collaborative robots are multirobot systems working together for the same industrial task such as robotic assembling. To achieve an efficient collaboration, robots require not only locally sensing the environmental data but also immediately sharing these data with neighbors. However, there exists a dilemma between the large amount of sensory data and the limited wireless bandwidth. In this paper, we study the problem of maximizing the throughput of sensory data sharing in collaborative robots. This data sharing is different from the transmissions in conventional mobile networks due to the real-time sharing requirement and the vicinity sharing pattern. Thus, existing adaptation methods cannot be applied directly. To maximize the throughput in dynamic environment, we propose a novel adaptation method AdaSharing based on control theory, which jointly adapts the combination of packet rate and transmission power according to the feedback of throughput. We implement AdaSharing in a nine-robot testbed, and conduct extensive experiments to verify its feasibility and effectiveness. Simulations based on ns-2 are further conducted to evaluate AdaSharing in large-scale scenarios. Both experiment and simulation results demonstrate that AdaSharing outperforms existing methods by improving the throughput up to 23%.