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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Bibliographic Details
Published in:	Aerospace Vol. 11; no. 1; p. 99
Main Authors:	Lou, Junlin, Yuksek, Burak, Inalhan, Gokhan, Tsourdos, Antonios
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2024
Subjects:	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
Online Access:	Get full text
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