A Vision Navigation Method for Agricultural Machines Based on a Combination of an Improved MPC Algorithm and SMC

Yuting Zhai, Dongyan Huang, Jian Li, Xuehai Wang and Yanlei Xu ()
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Yuting Zhai: School of Information Technology, Jilin Agricultural University, Changchun 130118, China
Dongyan Huang: School of Engineering Technology, Jilin Agricultural University, Changchun 130118, China
Jian Li: School of Information Technology, Jilin Agricultural University, Changchun 130118, China
Xuehai Wang: School of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yanlei Xu: School of Engineering Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 21, 1-22

Abstract: Vision navigation systems provide significant advantages in agricultural scenarios such as pesticide spraying, weeding, and harvesting by interpreting crop row structures in real-time to establish guidance lines. However, the delay introduced by image processing causes the path and pose information relied upon by the controller to lag behind the actual vehicle state. In this study, a hierarchical delay-compensated cooperative control framework (HDC-CC) was designed to synergize Model Predictive Control (MPC) and Sliding Mode Control (SMC), combining predictive optimization with robust stability enforcement for agricultural navigation. An upper-layer MPC module incorporated a novel delay state observer that compensated for visual latency by forward-predicting vehicle states using a 3-DoF dynamics model, generating optimized front-wheel steering angles under actuator constraints. Concurrently, a lower-layer SMC module ensured dynamic stability by computing additional yaw moments via adaptive sliding surfaces, with torque distribution optimized through quadratic programming. Under varying adhesion conditions tests demonstrated error reductions of 74.72% on high-adhesion road and 56.19% on low-adhesion surfaces. In Gazebo simulations of unstructured farmland environments, the proposed framework achieved an average path tracking error of only 0.091 m. The approach effectively overcame vision-controller mismatches through predictive compensation and hierarchical coordination, providing a robust solution for vision autonomous agricultural machinery navigation in various row-crop operations.

Keywords: agricultural tractors; vision autonomous guidance; model predictive control; path tracking (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
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