Resilient multi-robot target pursuit
Jiani Li, Waseem Abbas, Mudassir Shabbir, Xenofon Koutsoukos · 2020
We consider the problem of networked agents cooperating together to perform a task of optimizing the parameters of a global cost function. Agents receive linearly correlated noisy streaming data that can be used to learn the target parameters via Least-Mean-Squares (LMS) approaches. Diffusion scheme is incorporated such that at each step after agents adapt the parameters by the current received data, a combination step is included for agents to aggregate the information coming from its one-hop neighbors. It has been demonstrated that by introducing the aggregation step, diffusion algorithms greatly improve the learning accuracy of the parameters measured by the network Mean-Square-Deviation (MSD) [1].