Distributed Kalman Filters for Relative Formation Control of Mobile Agents

TL;DR

This paper develops distributed Kalman filters for multi-agent formation control that operate with relative position data, addressing gaps in existing literature by incorporating uncertainty models and providing robust estimation methods.

Contribution

It introduces statistically robust data models and optimal Kalman filters for relative formation control, bridging the gap between relative observation models and uncertainty considerations.

Findings

Distributed filters improve relative position estimation accuracy.

Simulations demonstrate the effectiveness of the proposed filters.

Framework highlights potential for enhanced multi-agent coordination.

Abstract

Formation control (FC) of multi-agent plays a critical role in a wide variety of fields. In the absence of absolute 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 models are rarely considered. In this article, we aim to bridge the gap between these domains, by exploring distributed filters tailored for relative FC of swarms. We propose statistically 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.