Autonomous path selection of unmanned aerial vehicle in dynamic environment using reinforcement learning

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dc.contributor.authorTamanakijprasart, Komsun
dc.contributor.authorPerrusquia, Adolfo
dc.contributor.authorMondal, Sabyasachi
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2025-03-05T16:57:29Z
dc.date.available2025-03-05T16:57:29Z
dc.date.freetoread2025-03-05
dc.date.issued2025-01-06
dc.date.pubOnline2025-01-03
dc.description.abstractThe Unmanned Aerial Vehicle (UAV) is an emerging area within the aviation industry. Currently, fully autonomous UAV operations in real-world scenarios are rare due to low technology readiness and a lack of trust. However, Artificial Intelligence (AI) offers powerful tools to adapt to changing conditions and handle complex perceptions. In autonomous vehicles, automotive self-driving technologies have made significant advances. To enhance the level of autonomy in aviation, it is beneficial to analyze these frameworks and extend autonomous driving principles to autonomous flying. This research introduces a novel solution for ensuring safe navigation in UAVs by adopting the concept of autonomous lane or path selection strategies used in cars. The approach employs deep reinforcement learning (DRL) for high-level decision-making in selecting the appropriate path generated by various established algorithms that consider different scenarios. Specifically, the Interfered Fluid Dynamical System (IFDS) \cite{IFDS_OG} is utilized for guidance and the PID for the flight control system. The UAV can choose between global and local paths and determine the appropriate speed for following these paths. This proposed framework lays the foundation for future research into practical and safe navigation strategies for UAVs.
dc.description.conferencenameAIAA SCITECH 2025 Forum
dc.identifier.citationTamanakijprasart K, Perrusquia A, Mondal S, Tsourdos A. (2025) Autonomous path selection of unmanned aerial vehicle in dynamic environment using reinforcement learning. In: AIAA SCITECH 2025 Forum, 6-10 January 2025, Orlando, FL, USA. Paper number AIAA 2025-1998
dc.identifier.elementsID564472
dc.identifier.paperNoAIAA 2025-1998
dc.identifier.urihttps://doi.org/10.2514/6.2025-1998
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23564
dc.language.isoen
dc.publisherAIAA
dc.publisher.urihttps://arc.aiaa.org/doi/10.2514/6.2025-1998
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4001 Aerospace Engineering
dc.subject46 Information and Computing Sciences
dc.subject40 Engineering
dc.subject4602 Artificial Intelligence
dc.titleAutonomous path selection of unmanned aerial vehicle in dynamic environment using reinforcement learning
dc.typeConference paper
dcterms.coverageOrlando, FL
dcterms.dateAccepted2024-08-26
dcterms.temporal.endDate10-Jan-2025
dcterms.temporal.startDate6-Jan-2025

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