Browsing by Author "Lou, Junlin"

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    Real-time on-the-fly motion planning for urban air mobility via updating tree data of sampling-based algorithms using neural network inference
    (MDPI, 2024-01-22) Lou, Junlin; Yuksek, Burak; Inalhan, Gokhan; Tsourdos, Antonios
    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.
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    ItemOpen Access
    An RRT* based method for dynamic mission balancing for urban air mobility under uncertain operational conditions
    (IEEE, 2021-11-15) Lou, Junlin; Yuksek, Burak; Inalhan, Gokhan; Tsourdos, Antonios
    Urban air mobility provides an enabling technology towards on-demand and flexible operations for passenger and cargo transportation in metropolitan areas. Electric vertical-takeoff and landing (eVTOL) concept is a potential candidate for urban air mobility platform because of its lower carbon emissions, lower noise generations and potentially lower operational costs. However, such a transportation model is subject to numerous complicated environmental and urban design factors including buildings, dynamic obstacles and micro-weather patterns. In addition, communication, navigation and surveillance quality-of-service and availability would be affected on the overall system performance and resilience. Some social factors such as privacy, noise and visual pollution should also be considered to provide a seamless integration of the urban air mobility applications into the daily life. This paper describes an integrated RRT* based approach for designing and executing flight trajectories for urban airspace subject to operating constraints, mission constraints, and environmental conditions. The generated path is energy-efficient and enables aerial vehicle to perform mid-flight landing for battery changing or emergency situations. Moreover, this paper proposes another approach that allows on-the-fly path re-planning under dynamic constraints such as geofences or micro-weather patterns. As such, the approach also provides a method toward contingency operations such as emergency landing on the fly.