Issue |
MATEC Web Conf.
Volume 410, 2025
2025 3rd International Conference on Materials Engineering, New Energy and Chemistry (MENEC 2025)
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Article Number | 04004 | |
Number of page(s) | 11 | |
Section | Intelligent Systems and Sensor Technologies for Autonomous Operations | |
DOI | https://doi.org/10.1051/matecconf/202541004004 | |
Published online | 24 July 2025 |
Design of an intelligent Navigation System for Unmanned Rice Transplanters Based on the Beidou Satellite Navigation System
1 College of Mechanical Engineering, Taiyuan University of Technology, 030000 Shanxi, China
2 Institute of Mechanical Engineering, Rail Transit and Intelligent Manufacturing Entrepreneurship, Changzhou University, 213164 Changzhou, China
3 Portland College, Nanjing University of Posts and Telecommunications, 210023 Nanjing, China
* Corresponding author: chenjialu8011@tyut.edu.cn
Agricultural mechanization is a vital indicator of agricultural modernization. Historical developments in China and abroad demonstrate that agricultural machinery has fundamentally transformed production methods. With the advancement of agricultural mechanization, improving production efficiency is crucial for ensuring food security. Rice, as one of China’s primary food crops, relies heavily on transplanting—a critical stage in rice cultivation. High-efficiency transplanting technology forms the foundation of rice production. However, traditional transplanters in China still suffer from low automation, slow operation, and inefficiency. Autonomous navigation technology, a core element of modern agricultural intelligence, can significantly enhance rice planting efficiency. This paper analyzes the feasibility of unmanned intelligent transplanter navigation technology based on an overview of traditional transplanter technologies and their limitations. A BeiDou Navigation Satellite System (BDS)-based autonomous navigation system for unmanned rice transplanters is proposed, integrating multi-sensor fusion. Subsequent optimization enables path planning for the transplanter. Finally, prospects for multi-machine collaboration in future smart agriculture are discussed.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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