Adaptive Algorithms for Coverage Control and Space Partitioning in Mobile Robotic Networks

TL;DR

This paper introduces distributed stochastic gradient algorithms for adaptive coverage, space partitioning, and routing in mobile robotic networks, enabling efficient, resource-friendly deployment without prior knowledge of event distributions.

Contribution

It presents a unified stochastic gradient framework for various deployment problems, including adaptive coverage with heterogeneous agents, simplifying existing solutions and broadening applicability.

Findings

Algorithms effectively optimize coverage and routing in unknown environments.

Distributed rules are simple and suitable for resource-limited platforms.

Framework generalizes and simplifies previous approaches.

Abstract

This paper considers deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Moreover, it is assumed that the event location distribution is a priori unknown, and can only be progressively inferred from the observation of the actual event occurrences. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. In each case, distributed stochastic gradient algorithms optimizing the performance objective are presented. The stochastic gradient view simplifies and generalizes previously proposed solutions, and is applicable to new complex scenarios, such as adaptive coverage involving heterogeneous agents. Remarkably, these algorithms often take the form of simple distributed rules that could be implemented on resource-limited platforms.