All Issue

2024 Vol.6, Issue 4 Preview Page

Research Article

Development of a feedforward-based path tracking algorithm with steering bias compensation for an auto-guided tractor
Yeon-Tae Kim14
,
Yong-Hyun Kim12
,
Hak-Jin Kim123*
1Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
2Integrated Major in Global Smart Farm, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
3Adjunct Researcher, Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
4GINT, Gyeonggi-do 13487, Korea
*Corresponding Author
31 December 2024. pp. 250-261
PDF XML Citation
Abstract

As the commercialization of self-driving agricultural machinery advances, domestic farmers are increasingly purchasing auto-guidance steering kits for agricultural operations. However, due to the characteristics of the hydrostatic steering system in tractors, oil leakage can lead to steering bias during operation. While a wheel angle sensor can mitigate this issue, they incur additional costs. Moreover, using a wheel angle sensor to correct steering bias may introduce a delay, particularly when disturbances occur during agricultural tasks. This study developed a compensation algorithm for an auto-guidance system that operates without a wheel angle sensor, ensuring both accuracy and stability in path tracking. The proposed compensation algorithm predicts steering bias based on motor encoder from the tractor’s electric power steering system, eliminating the need for a steering angle sensor. It employed a feedforward method to compensate for steering bias in real time, enabling rapid response to disturbances and addressing the inherent limitation of the hydrostatic steering system. To control path tracking, the stanley algorithm was fused with a PID controller. Field experiments were conducted with various tractor models equipped with an automatic steering system. The results demonstrated that the path tracking system, incorporating the proposed compensation algorithm, closely followed straight reference paths. The system achieved a root mean square error of lateral deviation as low as 2 cm and heading errors of less than 1.1 degrees, confirming its effectiveness and precision.

Keywords
Path tracking system
Hydrostatic steering system
Compensation algorithm
Steering bias
Auto-guided tractor
References
1

Adhikari, S. P., Kim, G., Kim, H. 2020. Deep neural network-based system for autonomous navigation in paddy field. IEEE Access 8, 71272-71278. https://doi.org/10.1109/ACCESS.2020.2987642

10.1109/ACCESS.2020.2987642
2

Han, X. Z., Kim, H. J., Kim, J. Y., Yi, S. Y., Moon, H. C., Kim, J. H., Kim, Y. J. 2015. Path-tracking simulation and field tests for an auto-guidance tillage tractor for a paddy field. Computers and Electronics in Agriculture 112: 161-171. https://doi.org/10.1016/j.compag.2014.12.025

10.1016/j.compag.2014.12.025
3

He, J., Hu, L., Wang, P., Liu, Y., Man, Z., Tu, T., Luo, X. 2022. Path tracking control method and performance test based on agricultural machinery pose correction. Computers and Electronics in Agriculture 200: 107185. https://doi.org/10.1016/j.compag.2022.107185

10.1016/j.compag.2022.107185
4

Huang, W., Ji, X., Wang, A., Wang, Y., Wei, X. 2023. Straight-Line path tracking control of agricultural tractor-trailer based on fuzzy sliding mode control. Applied Sciences 13(2): 872. https://doi.org/10.3390/app13020872

10.3390/app13020872
5

Jeon, C. W., Kim, H. J., Yun, C., Park, S. J., Hwang, Y. B., Han, X. 2024. Autonomous paddy field puddling and leveling operations based on full-coverage path generation and tracking. Precision Agriculture 25(1): 235-256. https://doi.org/10.1007/s11119-023-10066-0

10.1007/s11119-023-10066-0
6

Lee, C., Jeon, C. W., Han, X., Kim, J. H., Kim, H. J. 2022. Application of electrohydraulic proportional valve for steering improvement of an autonomous tractor. Journal of Biosystems Engineering 47(2): 167-180. https://doi.org/10.1007/s42853-022-00137-x

10.1007/s42853-022-00137-x
7

Li, Y., Efatmaneshnik, M., Dempster, A. G. 2012. Attitude determination by integration of MEMS inertial sensors and GPS for autonomous agriculture applications. GPS solutions 16: 41-52. https://doi.org/10.1007/s10291-011-0207-y

10.1007/s10291-011-0207-y
8

Miao, C., Chu, H., Cao, J., Sun, Z., Yi, R. 2018. Steering angle adaptive estimation system based on GNSS and MEMS gyro. Computers and Electronics in Agriculture 153: 196-201. https://doi.org/10.1016/j.compag.2018.08.019

10.1016/j.compag.2018.08.019
9

Miao, H., Diao, P., Yao, W., Li, S., Wang, W. 2022. Stability study of time lag disturbance in an automatic tractor steering system based on sliding mode predictive control. Agriculture 12(12): 2091. https://doi.org/10.3390/agriculture12122091

10.3390/agriculture12122091
10

Peng, C., Wei, P., Fei, Z., Zhu, Y., Vougioukas, S. G. 2024. Optimization‐based motion planning for autonomous agricultural vehicles turning in constrained headlands. Journal of Field Robotics 41(6): 1984-2008. https://doi.org/10.1002/rob.22374

10.1002/rob.22374
11

Reid, J. F., Zhang, Q., Noguchi, N., Dickson, M. 2000. Agricultural automatic guidance research in North America. Computers and electronics in agriculture 25(1-2): 155-167. https://doi.org/10.1016/S0168-1699(99)00061-7

10.1016/S0168-1699(99)00061-7
12

Rovira-Más, F. 2010. Sensor architecture and task classification for agricultural vehicles and environments. Sensors 10(12): 11226-11247. https://doi.org/10.3390/s101211226

10.3390/s10121122622163522PMC3231084
13

Sun, Y., Cui, B., Ji, F., Wei, X., Zhu, Y. 2022. The full-field path tracking of agricultural machinery based on PSO-enhanced fuzzy Stanley model. Applied Sciences 12(15): 7683. https://doi.org/10.3390/app12157683

10.3390/app12157683
14

Wang, P., Hu, L., He, J., Ke, S., Man, Z., Tu, T., Yang, L., Li, Y., Yi, Y., Li, W., Luo, X. 2022a. Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope. International Journal of Agricultural and Biological Engineering 15(6): 197-205. https://doi.org/10.25165/j.ijabe.20221506.7523

10.25165/j.ijabe.20221506.7523
15

Wang, L., Zhai, Z., Zhu, Z., Mao, E. 2022b. Path tracking control of an autonomous tractor using improved Stanley controller optimized with multiple-population genetic algorithm. Actuators 11(1): 22. https://doi.org/10.3390/act11010022

10.3390/act11010022
16

Yin, C., Wang, S., Li, X., Yuan, G., Jiang, C. 2020. Trajectory tracking based on adaptive sliding mode control for agricultural tractor. IEEE Access 8: 113021-113029. https://doi.org/10.1109/ACCESS.2020.3002814

10.1109/ACCESS.2020.3002814
Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 6
  • No :4
  • Pages :250-261
  • Received Date : 2024-12-07
  • Revised Date : 2024-12-19
  • Accepted Date : 2024-12-19
  • DOI :https://doi.org/10.22765/pastj.20240018
Journal Informaiton Precision Agriculture Science and Technology Precision Agriculture Science and Technology
Online Submission & Review System
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • KCI 문헌 유사도 검사
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close