Publication detail

Obstacle Avoidance in UAVs: Using a Bug-Inspired Algorithm and Neural Network-Based RGB Camera Collision Prediction

RAICHL, P. MARCOŇ, P. JANOUŠEK, J.

Original Title

Obstacle Avoidance in UAVs: Using a Bug-Inspired Algorithm and Neural Network-Based RGB Camera Collision Prediction

Type

article in a collection out of WoS and Scopus

Language

English

Original 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.

Keywords

UAV, Obstacle avoidance, Collision Prediction, Artificial Intelligence, Neural Networks

Authors

RAICHL, P.; MARCOŇ, P.; JANOUŠEK, J.

Released

23. 4. 2024

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-6231-1

Book

Proceedings I of the 30th Conference STUDENT EEICT 2024

Pages from

327

Pages to

331

Pages count

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"
}