Distributed State Estimation Using Intermittently Connected Robot Networks

TL;DR

This paper introduces a novel distributed state estimation method for multi-robot systems with intermittent communication, optimizing measurement collection and communication scheduling to minimize estimation uncertainty without requiring continuous network connectivity.

Contribution

It presents the first framework that relaxes all network connectivity assumptions and controls intermittent communication to improve estimation accuracy.

Findings

Significant improvement in estimation accuracy over continuous connectivity methods.

Effective communication scheduling ensures network intermittency while maintaining estimation quality.

Sampling-based motion planning optimizes robot positions for data collection.

Abstract

This paper considers the problem of distributed state estimation using multi-robot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines (i) communication schedules that ensure that the network is intermittently connected, and (ii) sampling-based motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first distributed state estimation framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.