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
Main Authors: Junlin Lou, Burak Yuksek, Gokhan Inalhan, Antonios Tsourdos
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Aerospace
Subjects:
Online Access:https://www.mdpi.com/2226-4310/11/1/99