Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

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 sepa...

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Vydáno v:Aerospace Ročník 11; číslo 1; s. 99
Hlavní autoři: Lou, Junlin, Yuksek, Burak, Inalhan, Gokhan, Tsourdos, Antonios
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2024
Témata:
Aircraft
Algorithms
Automation
Data mining
Efficiency
Flexibility
generative adversarial imitation learning
Geofences
Kinematics
Machine learning
Motion planning
Neural networks
Optimization
Planning
Real time
Recurrent neural networks
reinforcement learning
Trajectory optimization
Urban air
Urban air mobility
Vehicles
ISSN:2226-4310, 2226-4310
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Shrnutí: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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ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace11010099