Distributed Localization of Multiagent Systems With Imperfect Channels Based on Iterative Learning

The real-time localization problem for a class of multiagent systems with imperfect channels under directed graphs is investigated in this article. A successive distributed localization estimation algorithm based on iterative learning is proposed to solve the localization problem of the agent with bounded communication delays. The barycentric coordinates are used to represent the relative position between agents. An estimator is proposed to inspect whether the received data packet is the latest through the iteration stamp on the packet. Meanwhile, an intermittent distributed localization algorithm is devised to handle with the random data loss of agents in real-time localization problem. The relationship between the convergence rate and the probability of the data loss is given in the sense of mathematical expectations. Both numerical simulation and Qbot-2e robot experiment are provided to verify the effectiveness and the feasibility of the aforementioned methods.