EventsMOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
Published
This submission belongs to the session 05. AI.MED-08: AI, Neuro Sciences, Med. Info., & Biomed. Eng. Congress, Coruña, Spain-Carleton, Canada-Stanford, USA, 2021 of the event MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
Published date
23 Nov, 2021
Academic Editor
author-avatarHumbert G. Díaz
Citation
Alejandro Puente-Castro, Daniel Rivero Cebrián, Alejandro Pazos Sierra, Enrique Fernandez-Blanco, Artificial Intelligence techniques for autonomous drone swarms, in Proceedings of MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed., 25 January–30 December 2021, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-07-11845
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Artificial Intelligence techniques for autonomous drone swarms

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1. RNASA-IMEDIR
2. UDC
Abstract

Path planning is a critical problem that entails calculating a wide range of ideal paths for each drone in a swarm. If this challenge could be solved, it would be possible to control a large number of drones without the need for human involvement while preserving optimal trajectories. The fewer people needed to operate UAVs and the shorter the path, the lower the costs. The primary goal is to create Artificial Intelligence based systems that can calculate the best flying path for a swarm of drones. Regardless of the maps or the amount of drones in the swarm, the goal of these result paths is to accomplish comprehensive coverage of a flight area for tasks like agricultural prospection.

Keywords
reinforcement learning
uav
swarm
cell-grid
path planning
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