Robot for Navigation in Maize Crops for the Field Robot Event 2023
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.
Downloads
References
Bai, Y., Zhang, B., Xu, N., Zhou, J., Shi, J., & Diao, Z. (2023). Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review. Computers and Electronics in Agriculture , 205 , 107584.
Calicioglu, O., Flammini, A., Bracco, S., Bellù, L., & Sims, R. (2019). The future challenges of food and agriculture: An Integrated analysis of trends and solutions. Sustainability, 11 (1), 222. Retrieved from: https://doi.org/10.3390/su11010222
Cizmic, D., Hoelbling, D., Baranyi, R., Breiteneder, R., & Grechenig, T. (2023). Smart boxing glove "RD a": IMU combined with force sensor for highly accurate technique and target recognition using machine learning. Applied Sciences , 13 (16), 1-16. Retrieved from: https://doi.org/10.3390/app13169073
Food and Agriculture Organization (FAO). (2009). How to feed the world in 2050. Retrieved from: https://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/Issues_papers_SP/La_agricultura_mundial.pdf
Feng, X., Liang, W. J., Chen, H. Z., Liu, X. Y., & Yan, F. (2023). Autonomous localization and navigation for agricultural robots in greenhouse. Wireless Personal Communications , 131 , 2039-2053. Retrieved from: https://doi.org/10.1007/s11277-023-10531-z
Fujita, S., Emaru, T., Ravankar, A. A., & Kobayashi, Y. (2020). Development of robust ridge detection method and control system for autonomous navigation of mobile robot in agricultural farm. In Symposium on Robot Design, Dynamics and Control (pp. 16-23). Cham: Springer International Publishing.
Jiang, G., Wang, Z., & Liu, H. (2015). Automatic detection of crop rows based on multi-ROIs. Expert Systems with Applications , 42 (5), 2429-2441. Retrieved from: https://doi.org/10.1016/j.eswa.2014.10.033
Khadatkar, A., Mathur, S. M., Dubey, K., & BhusanaBabu, V. (2021). Development of embedded automatic transplanting system in seedling transplanters for precision agriculture. Artificial Intelligence in Agriculture , 5 , 175-184. Retrieved from: https://doi.org/10.1016/j.aiia.2021.08.001
Kannan, M., Ismail, I., & Bunawan, H. (2018). Maize dwarf mosaic virus: From genome to disease management. Viruses , 10 (9), 492. Retrieved from: https://doi.org/10.3390/v10090492
Kurniawan, A. (2021). IMU sensor: Accelerometer and gyroscope. In: Beginning Arduino Nano 33 IoT. Apress, Berkeley, CA. Retrieved from: https://doi.org/10.1007/978-1-4842-6446-1_3
Makesense (2023). Makesense for Labeling [Software platform]. Retrieved from: https://www.makesense.ai/
Mao, S., Li, Y., Ma, Y., Zhang, B., Zhou, J., & Wang, K. (2020). Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion. Computers and Electronics in Agriculture , 170 , 105254. Retrieved from: https://doi.org/10.1016/j.compag.2020.105254
Monteiro, N., Alencar, E., Souza, N., & Leao, T. (2021). Ozonized water in the preconditioning of corn seeds: physiological quality and field performance. Ozone Science and Engineering , 43 (5), 436-450. Retrieved from: https://doi.org/10.1080/01919512.2020.1836472
Nehme, H., Aubry, C., Solatges, T., Savatier, X., Rossi, R., & Boutteau, R. (2021). Lidar-based structure tracking for agricultural robots: Application to autonomous navigation in vineyards. Journal of Intelligent & Robotic Systems , 103, 1-16. Retrieved from: https://doi.org/10.1007/s10846-021-01519-7
Orum, J., Wubale, T., Marcus, S., Harold, A., Veldhuisen, B., & Hildrands, H. (2023). Optimal use of agricultural robot in arable crop rotation: A case study from the Netherlands. Smart Agricultural Technology, 5, 1-8. Retrieved from: https://doi.org/10.1016/j.atech.2023.100261
Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Retrieved from: https://arxiv.org/abs/1612.08242
Reyes-Amador, A., & Velázquez-López, N. (2019). Modelo industrial de carrocería para vehículo de cuatro ruedas (No. de solicitud MX/f/2018/003352). IMPI. Retrieved from: https://siga.impi.gob.mx/
Reyes-Amador, A., & Velázquez-López, N. (2020). Sistema de suspensión para vehículos terrestres autónomos o no autónomos (Patente No. MX 4369 B). IMPI. Retrieved from: https://siga.impi.gob.mx/
Saavedra Sueldo, C., Perez Colo, I., De Paula, M., Villar, S. A., & Acosta, G. G. (2023). ROS-based architecture for fast digital twin development of smart manufacturing robotized systems. Annals of Operations Research, 322 (1), 75-99. Retrieved from: https://doi.org/10.1007/s10479-022-04759-4 Solawetz, J. (2020). What is YOLOv5? A Guide for Beginners.
Roboflow. Retrieved from:https://blog.roboflow.com/yolov5-improvements-and-evaluation/
Subeesh, A., & Mehta, C. R. (2021). Automation and digitization of agriculture using artificial intelligence and internet of things. Artificial Intelligence in Agriculture , 5 , 278-291. Retrieved from: https://doi.org/10.1016/j.aiia.2021.11.004
Xie, D., Chen, L., Liu, L., Chen, L., & Wang, H. (2022). Actuators and sensors for application in agricultural robots: A review. Machines , 10 (10), 913. Retrieved from: https://doi.org/10.3390/machines10100913
Copyright (c) 2024 David Iván Sánchez-Chávez, Noé Velázquez-López, Guillermo García-Sánchez, Alan Hernández-Mercado, Omar Alexis Avendaño-Lopez, Mónica Elizabeth Berrocal-Aguilar
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.