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Detail publikace
RAICHL, P. MARCOŇ, P. JANOUŠEK, J.
Originální název
Obstacle Avoidance in UAVs: Using a Bug-Inspired Algorithm and Neural Network-Based RGB Camera Collision Prediction
Typ
článek ve sborníku mimo WoS a Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
UAV, Obstacle avoidance, Collision Prediction, Artificial Intelligence, Neural Networks
Autoři
RAICHL, P.; MARCOŇ, P.; JANOUŠEK, J.
Vydáno
23. 4. 2024
Nakladatel
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Místo
Brno
ISBN
978-80-214-6231-1
Kniha
Proceedings I of the 30th Conference STUDENT EEICT 2024
Strany od
327
Strany do
331
Strany počet
5
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf
BibTex
@inproceedings{BUT189116, author="Petr {Raichl} and Petr {Marcoň} and Jiří {Janoušek}", title="Obstacle Avoidance in UAVs: Using a Bug-Inspired Algorithm and Neural Network-Based RGB Camera Collision Prediction", booktitle="Proceedings I of the 30th Conference STUDENT EEICT 2024", year="2024", pages="327--331", publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií", address="Brno", isbn="978-80-214-6231-1", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf" }