Quantification and Optimization of Straight-Line Attitude Control for Orchard Weeding Robots Using Adaptive Pure Pursuit

Weidong Jia, Zhenlei Zhang, Xiang Dong, Mingxiong Ou, Ronghua Gao, Yunfei Wang, Qizhi Yang and Xiaowen Wang ()
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Weidong Jia: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhenlei Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xiang Dong: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Mingxiong Ou: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Ronghua Gao: School of Intelligent Application Engineering, Jinshan Vocational Technical College, Zhenjiang 212200, China
Yunfei Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Qizhi Yang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaowen Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 19, 1-13

Abstract: In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44° 2 ), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization.

Keywords: orchard weeding robot; Pure Pursuit; adaptive lookahead; yaw control; time-series behavior evaluation (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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