Energy consumption optimisation for unmanned aerial vehicle based on reinforcement learning framework

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dc.contributor.authorWang, Ziyue
dc.contributor.authorXing, Yang
dc.date.accessioned2024-06-20T11:49:52Z
dc.date.available2024-06-20T11:49:52Z
dc.date.freetoread2025-04-17
dc.date.issued2024-04-16
dc.description.abstractThe average battery life of drones in use today is around 30 minutes, which poses significant limitations for ensuring long-range operation, such as seamless delivery and security monitoring. Meanwhile, the transportation sector is responsible for 93% of all carbon emissions, making it crucial to control energy usage during the operation of UAVs for future net-zero massive-scale air traffic. In this study, a reinforcement learning (RL)-based model was implemented for the energy consumption optimisation of drones. The RL-based energy optimisation framework dynamically tunes vehicle control systems to maximise energy economy while considering mission objectives, ambient circumstances, and system performance. RL was used to create a dynamically optimised vehicle control system that selects the most energy-efficient route. Based on training times, it is reasonable to conclude that a trained UAV saves between 50.1% and 91.6% more energy than an untrained UAV in this study by using the same map.en_UK
dc.identifier.citationWang Z, Xing Y. (2024) Energy consumption optimisation for unmanned aerial vehicle based on reinforcement learning framework. International Journal of Powertrains, Volume 13, Issue 1, March 2024, pp.75-94en_UK
dc.identifier.eissn1742-4275
dc.identifier.issn1742-4267
dc.identifier.urihttps://dx.doi.org/10.1504/IJPT.2024.138001
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22536
dc.language.isoen_UKen_UK
dc.publisherInderscienceen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectPower consumptionen_UK
dc.subjectMachine Learningen_UK
dc.subjectReinforcement Learningen_UK
dc.subjecttrajectory optimizationen_UK
dc.subjectQ- Learningen_UK
dc.subjectenergy efficiencyen_UK
dc.subjectpath planningen_UK
dc.titleEnergy consumption optimisation for unmanned aerial vehicle based on reinforcement learning frameworken_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-05-03

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