Optimal Multi-robot Formations for Relative Pose Estimation Using Range Measurements

TL;DR

This paper investigates how to optimize multi-robot formations to maximize the accuracy of relative pose estimation from range measurements, demonstrating significant improvements through formation optimization.

Contribution

It introduces a method to optimize robot formations based on Fisher information to enhance relative pose estimation accuracy.

Findings

Optimized formations significantly improve estimation accuracy.

Simulation and experiments validate the formation optimization approach.

Large accuracy gains are achieved by geometry optimization.

Abstract

In multi-robot missions, relative position and attitude information between agents is valuable for a variety of tasks such as mapping, planning, and formation control. In this paper, the problem of estimating relative poses from a set of inter-agent range measurements is investigated. Specifically, it is shown that the estimation accuracy is highly dependent on the true relative poses themselves, which prompts the desire to find multi-agent formations that provide the best estimation performance. By direct maximization of Fischer information, it is shown in simulation and experiment that large improvements in estimation accuracy can be obtained by optimizing the formation geometry of a team of robots.