Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method

Jiawei Zhou, Junhao Wen, Liwen Yao, Zidong Yang, Lijun Xu and Lijian Yao ()
Additional contact information
Jiawei Zhou: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Junhao Wen: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Liwen Yao: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Zidong Yang: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Lijun Xu: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Lijian Yao: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China

Agriculture, 2025, vol. 15, issue 3, 1-18

Abstract: The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. To address the issue of low tracking accuracy caused by variable-curvature paths in automatic navigation within agricultural environments, this study proposes a fuzzy control-based path-tracking method. Firstly, a pure-pursuit model and a kinematic model were established based on a Four-Wheel Independent Steering and Four-Wheel Independent Driving (4WIS-4WID) structure. Secondly, a fuzzy controller with three inputs and one output was designed, using the lateral deviation, d e ; heading deviation, θ e ; and bending degree, c , of the look-ahead path as the input variables. Through multiple simulations and adjustments, 75 control rules were developed. The look-ahead distance, Ld , was obtained through fuzzification, fuzzy inference, and defuzzification processes. Next, a speed-control function was constructed based on the agricultural machinery’s pose deviations and the bending degree of the look-ahead path to achieve variable speed control. Finally, field tests were conducted to verify the effectiveness of the proposed path-tracking method. The tracking experiment results for the two types of paths indicate that under the variable-speed dynamic look-ahead distance strategy, the average lateral deviations for the variable-curvature paths were 1.8 cm and 3.3 cm while the maximum lateral deviations were 10.1 cm and 10.5 cm, respectively. Compared to the constant-speed fixed look-ahead pure-pursuit model, the average lateral deviation was reduced by 56.1% and the maximum lateral deviation by 50.4% on the U-shaped path. On the S-shaped path, the average lateral deviation was reduced by 56.0% and the maximum lateral deviation by 58.9%. The proposed method effectively improves the path-tracking accuracy of agricultural machinery on variable-curvature paths, meeting the production requirements for curved operations in agricultural environments.

Keywords: agricultural environment; autonomous navigation; fuzzy control; dynamic look-ahead distance; speed-control model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/3/266/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/3/266/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:3:p:266-:d:1577490

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().