Obstacle Avoidance in UAVs: Using a Bug-Inspired Algorithm and Neural Network-Based RGB Camera Collision Prediction
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Date
2024
Authors
Raichl, Petr
Marcoň, Petr
Janoušek, Jiří
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Mark
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex environments for various applications, necessitating advanced obstacle avoidance capabilities to ensure safety and mission success. Inspired by the simplicity and effectiveness of biological navigation strategies, this study introduces a novel approach to UAV obstacle avoidance, leveraging the principles of the bug algorithm combined with the predictive power of neural networks. We propose a hybrid model that integrates a lightweight neural network to predict potential collisions in real-time. Our methodology employs a two-stage process: first, the neural network assesses the immediate risk of collision; second, the bug algorithm-inspired decision-making process determines the UAV’s maneuvering actions to navigate without crashing to obstacles. The approach was tested both in simulation and real outdoor experiments.
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Citation
Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers. s. 327-331. ISBN 978-80-214-6231-1
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf
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Peer-reviewed
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií