Distributed Kalman Filters for Relative Formation Control of Multi-Agent Systems

Formation control (FC) of multi -agent systems plays a crit-ical role in a wide variety of fields. In the absence of abso-lute positioning, agents in FC systems rely on relative position measurements with respect to their neighbors. In distributed filter design literature, relative observation models are comparatively unexplored, and in FC literature, uncertainty mod-els are rarely considered. In this article, we aim to bridge the gap between these domains, by exploring distributed fil-ters tailored for relative FC of swarms. We propose statis-tically robust data models for tracking relative positions of agents in a FC network, and subsequently propose optimal Kalman filters for both centralized and distributed scenarios. Our simulations highlight the benefits of these estimators, and we identify future research directions based on our proposed framework.