UAV search path planning for livestock monitoring - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

UAV search path planning for livestock monitoring

Résumé

Unmanned Aerial Vehicles (UAVs) are being extensively deployed in numerous Livestock Management applications such as cattle disease diagnosis, counting and behavioral monitoring from videos and images captured by drones. The paper focuses on one increasingly important family of applications, UAV-assisted cattle monitoring applications where the objective is to remotely acquire some health state information from IoT nodes attached to the herding cattle. Such livestock data acquisition applications have many challenges. One of these challenges, which is the focus of this paper, is the problem that the target cattle position may not be known precisely, and might be defined with a large area. To address this issue, we design a formulation of this UAV-cattle search path problem as a mathematical optimization problem and show how it can be derived from other well-known formulation and related literature. A Mixed-Integer linear Programming (MILP) formulation is introduced to minimize the expected search time while covering all the search area to efficiently locate the animal. This formulation exploits a cattle position probability distribution map. The results show that the suggested approach yields excellent results using the existing MILP solvers.
Fichier principal
Vignette du fichier
Uav_search_path_planning_for_livestock_monitoring.pdf (329.98 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03935450 , version 1 (11-01-2023)

Identifiants

Citer

Najoua Benalaya, Cedric Adjih, Anis Laouiti, Ichrak Amdouni, Leila Saidane. UAV search path planning for livestock monitoring. 2022 IEEE 11th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired Networks (PEMWN), Nov 2022, Rome, Italy. pp.1-6, ⟨10.23919/PEMWN56085.2022.9963839⟩. ⟨hal-03935450⟩
32 Consultations
68 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More