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...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-01-01
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Series: | Aerospace |
Subjects: | |
Online Access: | https://www.mdpi.com/2226-4310/11/1/99 |