A Consensus-based Adaptive Data Sharing Algorithm For Multi-robots

With the rapid development of artificial intelligence and automation technology,robots have been widely applied in various fields.Compared with the single robot case,swarm robots have more strength in executing tasks by cooperation.Multi-robot collaboration currently faces many challenges,and the communication between robots is a thorny issue.In order to solve the problem of communication environment limitation faced by multi-robot collaboration,this paper focuses on consensus-based data sharing algorithms.The main work and innovations include:We design and implement a communication simulation tool for multi-robots under the ROS platform.we design and implement a multi-robot communication simulation tool in the ROS software framework.This tool selects the packet error rate as the communication quality metric,simulates communication channels and packet loss,and provides visualization and playback capabilities,which are the functions that is not provided by existing ROS simulator.The experimental results prove the effectiveness of the tool.We propose a consensus-based adaptive data sharing algorithm.In response to the communication constraints faced in UAV swarm coordination,both the lazy and the eager strategies were proposed to enable swarm-wide reliable information exchange to further behavior coordination for UAV swarms.However,these two algorithms are only evaluated in a fixed and homogeneous network scenario.Hence,how to choose the proper information exchange strategy for a UAV swarm in realistic dynamic and heterogeneous network environments remains an open while interesting problem.Therefore,in this paper,we first evaluate the convergence and payload cost of both strategies for robotic swarms in realistic network scenarios.Then we propose a novel online adaptive information exchange strategy by adopting single relay selection schemes to ensure low payload and fast convergence in various network environments.Numerical results reveal our novel strategy performs well across different network scenarios in terms of convergence and payload cost,showing its robustness,adaptive capability and potential applications in UAV swarms.