Action robust reinforcement learning for air mobility deconfliction against conflict induced spoofing

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dc.contributor.authorPanda, Deepak Kumar
dc.contributor.authorGuo, Weisi
dc.date.accessioned2024-10-14T13:42:21Z
dc.date.available2024-10-14T13:42:21Z
dc.date.freetoread2024-10-14
dc.date.issued2024-12
dc.date.pubOnline2024-09-17
dc.description.abstractIncreased dynamic drone usage has increased complexity in aerial navigation and often demands distributed local deconfliction. Due to the high velocities and few landmarks, robust deconfliction relies on precise positioning and synchronization. However, intentional spoofing attacks aimed at inducing navigation conflicts threaten the reliability of conventional techniques. Here, we address these concerns by establishing a baseline on the impact of novel conflict-inducing spoofing attacks on existing geometric navigation methods. Based on the impact of the attacks on the navigation, reinforcement learning (RL) strategy is used to counter the effects of spoofing attacks. In order to counter the effect of spoofing in randomized dynamic airspace conditions, a zero-sum action-robust (ZSAR) RL based on mixed Nash equilibrium objective is used. The proposed methodology yields an improved number of conflict-free paths while reducing average conflicts compared to existing state of the art RL strategies, thus making it suitable for deploying autonomous aircrafts.
dc.description.journalNameIEEE Transactions on Intelligent Transportation Systems
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)
dc.description.sponsorshipThis work was supported in part by the Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research under Grant ICRF2324-7-150; in part by the Engineering and Physical Sciences Research Council (EPSRC); and in part by the U.K. Research and Innovation (UKRI), “TAS-S: Trustworthy Autonomous Systems: Security” under Grant EP/V026763/1.
dc.format.extent21343-21355
dc.identifier.citationPanda DK, Guo W. (2024) Action robust reinforcement learning for air mobility deconfliction against conflict induced spoofing. IEEE Transactions on Intelligent Transportation Systems, Volume 25, Issue 12, December 2024, pp. 21343-21355
dc.identifier.eissn1558-0016
dc.identifier.elementsID554045
dc.identifier.issn1524-9050
dc.identifier.issueNo99
dc.identifier.issueNo12
dc.identifier.urihttps://doi.org/10.1109/tits.2024.3454354
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23038
dc.identifier.volumeNo25
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.urihttps://ieeexplore.ieee.org/document/10682497
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAircraft
dc.subjectAutonomous aerial vehicles
dc.subjectHeuristic algorithms
dc.subjectAircraft navigation
dc.subjectAir traffic control
dc.subjectGlobal Positioning System
dc.subjectArtificial intelligence
dc.subjectSpoofing
dc.subjectconflict resolution
dc.subjectreinforcement learning
dc.subjectUAV
dc.subjectadversarial network
dc.subjectdeconfliction
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subjectLogistics & Transportation
dc.subject3509 Transportation, logistics and supply chains
dc.subject4602 Artificial intelligence
dc.subject4603 Computer vision and multimedia computation
dc.titleAction robust reinforcement learning for air mobility deconfliction against conflict induced spoofing
dc.typeArticle
dc.type.subtypeArticle
dc.type.subtypeEarly Access
dcterms.dateAccepted2024-08-30

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