TY - JOUR
T1 - Formation Analysis for a Fleet of Drones
T2 - 17th International Conference on Agents and Artificial Intelligence, ICAART 2025
AU - Traversi, Emiliano
AU - Barcis, Michal
AU - Bellone, Lorenzo
AU - Barcis, Agata
AU - Ahmim-Bonaldi, Dina
AU - Ferrante, Eliseo
AU - Natalizio, Enrico
N1 - Publisher Copyright:
© 2025 by SCITEPRESS– Science and Technology Publications, Lda.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Formation Study
KW - Robot and Multi-Robot Systems
KW - Task Planning and Execution
UR - http://www.scopus.com/inward/record.url?scp=105001708756&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105001708756&partnerID=8YFLogxK
U2 - 10.5220/0013189400003890
DO - 10.5220/0013189400003890
M3 - Conference article
AN - SCOPUS:105001708756
SN - 2184-3589
VL - 1
SP - 471
EP - 480
JO - International Conference on Agents and Artificial Intelligence
JF - International Conference on Agents and Artificial Intelligence
Y2 - 23 February 2025 through 25 February 2025
ER -