Version 1
: Received: 29 November 2023 / Approved: 30 November 2023 / Online: 30 November 2023 (10:35:44 CET)
How to cite:
Svyatov, K.; Zhitkov, R.; Mikhailov, V.; Khayrullin, I. Methods and Software Tools for the Safety Movement of Agricultural Highly Automated Vehicle Based on Deep Learning Methods. Preprints2023, 2023111922. https://doi.org/10.20944/preprints202311.1922.v1
Svyatov, K.; Zhitkov, R.; Mikhailov, V.; Khayrullin, I. Methods and Software Tools for the Safety Movement of Agricultural Highly Automated Vehicle Based on Deep Learning Methods. Preprints 2023, 2023111922. https://doi.org/10.20944/preprints202311.1922.v1
Svyatov, K.; Zhitkov, R.; Mikhailov, V.; Khayrullin, I. Methods and Software Tools for the Safety Movement of Agricultural Highly Automated Vehicle Based on Deep Learning Methods. Preprints2023, 2023111922. https://doi.org/10.20944/preprints202311.1922.v1
APA Style
Svyatov, K., Zhitkov, R., Mikhailov, V., & Khayrullin, I. (2023). Methods and Software Tools for the Safety Movement of Agricultural Highly Automated Vehicle Based on Deep Learning Methods. Preprints. https://doi.org/10.20944/preprints202311.1922.v1
Chicago/Turabian Style
Svyatov, K., Vladislav Mikhailov and Imil Khayrullin. 2023 "Methods and Software Tools for the Safety Movement of Agricultural Highly Automated Vehicle Based on Deep Learning Methods" Preprints. https://doi.org/10.20944/preprints202311.1922.v1
Abstract
When implementing a control system for a ground-based unmanned vehicle (UV) used in agri-culture, an important factor is the low cost of the equipment used, so the use of cameras without lidars for navigation, avoiding obstacles, and ensuring movement safety seems promising. En-suring the safety of movement of UV consists of developing methods and means that make it possible to bypass static and dynamic obstacles and make an emergency stop if it is impossible to bypass.
The article describes methods and software that provide detection of objects surrounding the UV through the use of the YOLOv8 neural network, space free for movement through the use of se-mantic segmentation with the MobileNetV3 network architecture, tools for combining several data sources to build a local perception map, as well as linear and angular speed of the UV based on calculating the optimal direction of movement for avoiding obstacles. Testing of the developed methods and software was realized in the Webots simulation environment. Software is developed with ROS2.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
