Formation Analysis for a Fleet of Drones: A Mathematical Framework

Emiliano Traversi, Michal Barcis, Lorenzo Bellone, Agata Barcis, Dina Ahmim-Bonaldi, Eliseo Ferrante, Enrico Natalizio

Research output: Contribution to journalConference articlepeer-review

Abstract

Weconsider a dynamic coverage scenario, where a group of agents (e.g., Unmanned Aerial Vehicles (UAVs)) is exploring an environment in search of a moving target (e.g., survivors on a lifeboat). We assume UAVs are capable to achieve, maintain, and move in formation (e.g., to maintain connectivity). This paper addresses the question “Which formation maximizes the chance of finding the target?”. We propose a mathematical framework to answer this question. The proposed framework is generic and can be easily applied to various formations and missions. We show how the framework can identify which formation will result in better performance in the type of missions we consider. We analyze how different factors, namely the target speed relative to the group, affect the performance of the formations. We validate the framework against simulations of the considered scenarios. The supplementary video material including the real-world implementation is available at https://youtu.be/ mYmTnAJi-I?si=dSmVVNZOjj5NbSG1.

Original languageEnglish (US)
Pages (from-to)471-480
Number of pages10
JournalInternational Conference on Agents and Artificial Intelligence
Volume1
DOIs
StatePublished - 2025
Event17th International Conference on Agents and Artificial Intelligence, ICAART 2025 - Porto, Portugal
Duration: Feb 23 2025Feb 25 2025

Keywords

  • Formation Study
  • Robot and Multi-Robot Systems
  • Task Planning and Execution

ASJC Scopus subject areas

  • Artificial Intelligence

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