ArticleSensors (Basel, Switzerland)2021
A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Node Selection and Path Optimization for Passive Target Localization via UAVs.Sensors (Basel, Switzerland) · 2025Article
- Binocular stereo vision-based relative positioning algorithm for drone swarm.Scientific reports · 2025Article
- Lightweight air-to-air unmanned aerial vehicle target detection model.Scientific reports · 2024Article
- Synchronous Control of a Group of Flying Robots Following a Leader UAV in an Unfamiliar Environment.Sensors (Basel, Switzerland) · 2023Article
- Drone-Based 3D Synthetic Aperture Radar Imaging with Trajectory Optimization.Sensors (Basel, Switzerland) · 2022Article
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2 authors.
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Abstract
This article presents a framework for planning a drone swarm mission in a hostile environment. Elements of the planning framework are discussed in detail, including methods of planning routes for drone swarms using mixed integer linear programming (MILP) and methods of detecting potentially dangerous objects using EO/IR camera images and synthetic aperture radar (SAR). Methods of detecting objects in the field are used in the mission planning process to re-plan the swarm's flight paths. The route planning model is discussed using the example of drone formations managed by one UAV that communicates through another UAV with the ground control station (GCS). This article presents practical examples of using algorithms for detecting dangerous objects for re-planning of swarm routes. A novelty in the work is the development of these algorithms in such a way that they can be implemented on mobile computers used by UAVs and integrated with MILP tasks. The methods of detection and classification of objects in real time by UAVs equipped with SAR and EO/IR are presented. Different sensors require different methods to detect objects. In the case of infrared or optoelectronic sensors, a convolutional neural network is used. For SAR images, a rule-based system is applied. The experimental results confirm that the stream of images can be analyzed in real-time.
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