| Title: | Robot for navigation in maize crops for the Field Robot Event 2023 |
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| Authors: | ID Sánchez-Chávez, David Iván (Author)
ID Velázquez-López, Noé (Author)
ID García-Sánchez, Guillermo (Author)
ID Hernández-Mercado, Alan (Author)
ID Avendaño-Lopez, Omar Alexis (Author)
ID Berrocal-Aguilar, Mónica Elizabeth (Author) |
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| Files: |  https://journals.um.si/index.php/agricultura/article/view/4546
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FKBV - Faculty of Agriculture and Life Sciences
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| Abstract: | Navigation in a maize crop is a crucial task for the development of autonomous robots in agriculture, with numerous applications such as spraying, monitoring plant growth and health, and detecting weeds and pests. The Field Robot Event 2023 (FRE) continued to challenge universities and other research teams to push the development of algorithms for agricultural robots further. The Universidad Autónoma Chapingo has been developing a robot for various agricultural tasks, aiming to provide a low-cost alternative to work with Mexican farmers in the future. For this edition of the FRE, a navigation algorithm was created using an encoder, an IMU (Inertial Measurement Unit), an RPLIDAR (Rotating Platform Light Detection and Ranging), and cameras to collect data for decision-making. The algorithm was developed in ROS Melodic, dividing the task into steps that were tested to determine the robot's actual movements. The system navigates by using ROIs (regions of interest) and the mass center to guide the robot between maize rows. It calculates the mean of the final orientation values before reaching the end of a row, which is detected using an RPLIDAR. For turns and straight-line movements to reach the next row, the orientation is used as a guide. To detect plants for spraying, lasers located on each side of the vehicle are employed. Obstacle detection relies on a YOLOv5 (You Only Look Once) trained model and a laser, while reverse navigation uses a rear camera. During the competition, the robot faced challenges such as dealing with grass, the small size of the plants, and the need to use a different power source, which affected its performance. |
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| Keywords: | machine vision, convolutional neural network (CNN), regions of interest (ROI), autonomous navigation |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.06.2024 |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 35-46 |
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| Numbering: | Vol. 21, no. 1 |
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| PID: | 20.500.12556/DKUM-92585  |
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| UDC: | 633.15:004-9 |
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| ISSN on article: | 2820-610X |
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| COBISS.SI-ID: | 203599107 |
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| DOI: | 10.18690/agricsci.21.1.4 |
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| Publication date in DKUM: | 23.04.2025 |
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| Views: | 162 |
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| Downloads: | 4 |
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| Metadata: |    |
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| Categories: | Misc.
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