Real-time on-the-fly motion planning for urban air mobility via updating tree data of sampling-based algorithms using neural network inference

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dc.contributor.authorLou, Junlin
dc.contributor.authorYuksek, Burak
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2024-02-10T16:42:36Z
dc.date.available2024-02-10T16:42:36Z
dc.date.issued2024-01-22
dc.description.abstractIn this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach that we propose is a decoupled method that includes designing a policy network based on a recurrent neural network for a reinforcement learning algorithm, and then combining an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using a generative adversarial imitation learning algorithm for training a recurrent-neural-network-based policy network and generating the time-optimized trajectory. The simulation results show that our approaches have a short computation time when compared to other algorithms with similar performance while guaranteeing sufficient exploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles, and the re-planned trajectories maintain continuity for the executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling one to perform an on-the-fly update of the final landing position and to optimize the path and trajectory in real-time while keeping explorations in the environment.en_UK
dc.identifier.citationLou J, Yuksek B, Inalhan G, Tsourdos A. (2024) Real-time on-the-fly motion planning for urban air mobility via updating tree data of sampling-based algorithms using neural network inference. Aerospace, Volume 11, Issue 1, January 2024, Article number 99en_UK
dc.identifier.issn2226-4310
dc.identifier.urihttps://doi.org/10.3390/aerospace11010099
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20770
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmotion planningen_UK
dc.subjecturban air mobilityen_UK
dc.subjectmachine learningen_UK
dc.subjectreinforcement learningen_UK
dc.subjectgenerative adversarial imitation learningen_UK
dc.titleReal-time on-the-fly motion planning for urban air mobility via updating tree data of sampling-based algorithms using neural network inferenceen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-01-10

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