Publication | Open Access
Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey
879
Citations
255
References
2018
Year
EngineeringOptimization ApproachesFlying RobotUnmanned VehicleOperations ResearchUnmanned Aircraft ControlUnmanned SystemDrone SurveyingSystems EngineeringUnmanned Aerial VehiclesUnmanned Aircraft DynamicsManned VehiclesFlight OptimizationAerial DronesAerial RoboticsAerospace EngineeringCivil ApplicationsUnmanned Aerial SystemsAir Vehicle System
Unmanned aerial vehicles are an emerging technology with significant market potential, offering cost savings in infrastructure monitoring, agriculture, and deliveries, and can save lives in disaster management, medical transport, and environmental monitoring. The article surveys optimization approaches for civil UAV applications to serve as a quick entry point for researchers and planners. It reviews more than 200 articles, outlining key UAV applications, operational characteristics, and prevalent modeling techniques. The review concludes with recommendations for future research directions.
Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.
| Year | Citations | |
|---|---|---|
Page 1
Page 1