A Framework for Autonomous Robot Deployment with Perfect Demand Satisfaction using Virtual Forces

TL;DR

This paper introduces a two-stage virtual force-based framework for autonomous robot deployment that ensures perfect demand satisfaction and improves efficiency and fairness compared to existing methods.

Contribution

The paper presents a novel two-stage virtual force framework with a Trace Fingerprint technique for optimal deployment and a fairness-aware version for resource-constrained scenarios.

Findings

Outperforms existing approaches in deployment efficiency

Ensures perfect demand satisfaction through Trace Fingerprint

Achieves higher fairness in resource allocation

Abstract

In many applications, robots autonomous deployment is preferable and sometimes it is the only affordable solution. To address this issue, virtual force (VF) is one of the prominent approaches to performing multirobot deployment autonomously. However, most of the existing VF-based approaches consider only a uniform deployment to maximize the covered area while ignoring the criticality of specific locations during the deployment process. To overcome these limitations, we present a framework for autonomously deploy robots or vehicles using virtual force. The framework is composed of two stages. In the first stage, a two-hop Cooperative Virtual Force based Robots Deployment (Two-hop COVER) is employed where a cooperative relation between robots and neighboring landmarks is established to satisfy mission requirements. The second stage complements the first stage and ensures perfect demand satisfaction by utilizing the Trace Fingerprint technique which collected traces while each robot traversing the deployment area. Finally, a fairness-aware version of Two-hop COVER is presented to consider scenarios where the mission requirements are greater than the available resources (i.e. robots). We evaluate our framework via extensive simulations. The results demonstrate outstanding performance compared to contemporary approaches in terms of total travelled distance, total exchanged messages, total deployment time, and Jain fairness index.