A distributed estimation algorithm for collective behaviors in multiagent systems with applications to unicycle agents

摘要

In this paper, we propose a distributed estimation algorithm for the detection of collective behaviors in multiagent systems. Through local information exchange, the proposed algorithm can estimate the average of the integrals of local time-varying behavior signals of individual agents, such as network moments, which could serve as a feature indicator for group collective behaviors. Under the assumption that the communication network among agents is connected and bidirectional, the algorithm convergence is rigorously analyzed with an explicitly defined bounds of estimation errors. The event-triggering mechanism for information transmission is further employed for reducing the communication load. As a case study, the proposed distributed estimation algorithm is applied to detect the collective behaviors of unicycle agents. Simulation results on the estimation of network centroids are provided to illustrate the effectiveness of the method.