Error Threshold-Based Autonomous Navigation with Right-Angle Turning for Crawler-Type Combine Harvesters in Paddy Fields
Crawler-type combine harvesters feature labor-intensive operation, tough steering and complex environments in paddy fields, necessitating reliable automatic operation to ensure efficient and complete harvesting. An error threshold-based autonomous navigation system for crawler-type combine harvesters was developed by using right-angle turning according to unilateral brake steering. Based on the chassis structure and working principles, a moving control system was designed to achieve automatic control of steering, speed and throttle. A global path planning method was proposed to generate a spiral path by giving reference points and operation directions. A path tracking method based on the error threshold was developed to calculate both lateral and heading errors in real-time, and we executed the adjustment strategy to ensure rapid alignment and high-precision tracking. A right-angle turning method was implemented to prevent missed cutting and crop damage by giving an adjustment distance. Field tests showed that the maximum lateral and heading errors for straight-line path tracking were 10.25 cm and 1.94°, respectively. The maximum lateral and heading errors for right-angle turning were 17.64 cm and −14.46°, respectively. It was concluded that the newly developed autonomous navigation system showed adequate path tracking accuracy and stability, meeting working requirements in crop harvesting.
Authors
- Chao Jin (ORCID: https://orcid.org/0000-0001-6590-333X)
- Guangshun An
- Juan Du
- Xiang Yin
- Wenpeng Ma
Institutions
- Shandong University of Technology (CN)
- Ministry of Agriculture and Rural Affairs (CN)
- Nanjing Institute of Agricultural Mechanization (CN)
Publication Details
- Journal
- Agriculture
- Published
- 2025-12-24
- DOI
- https://doi.org/10.3390/agriculture16010042
- Primary Topic
- Agricultural Engineering and Mechanization
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Shandong University of Technology
- National Key Research and Development Program of China