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.author Lou, Junlin
dc.contributor.author Yuksek, Burak
dc.contributor.author Inalhan, Gokhan
dc.contributor.author Tsourdos, Antonios
dc.date.accessioned 2024-02-10T16:42:36Z
dc.date.available 2024-02-10T16:42:36Z
dc.date.issued 2024-01-22
dc.identifier.citation Lou 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 99 en_UK
dc.identifier.issn 2226-4310
dc.identifier.uri https://doi.org/10.3390/aerospace11010099
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/20770
dc.description.abstract In 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.language.iso en en_UK
dc.publisher MDPI en_UK
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject motion planning en_UK
dc.subject urban air mobility en_UK
dc.subject machine learning en_UK
dc.subject reinforcement learning en_UK
dc.subject generative adversarial imitation learning en_UK
dc.title Real-time on-the-fly motion planning for urban air mobility via updating tree data of sampling-based algorithms using neural network inference en_UK
dc.type Article en_UK


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