Design and Experiment of a Greenhouse Autonomous Following Robot Based on LQR–Pure Pursuit

Yibin Hu, Jieyu Xian, Maohua Xiao (), Qianzhe Cheng, Tai Chen, Yejun Zhu () and Guosheng Geng
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Yibin Hu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jieyu Xian: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Maohua Xiao: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Qianzhe Cheng: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Tai Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yejun Zhu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Guosheng Geng: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2025, vol. 15, issue 15, 1-31

Abstract: Accurate path tracking is crucial for greenhouse robots operating in complex environments. However, traditional curve tracking algorithms suffer from low tracking accuracy and large tracking errors. This study aim to develop a high precision greenhouse autonomous following robot, use ANSYS Workbench 19.2 to perform stress and deformation analysis on the robot, then propose a path tracking method based on Linear Quadratic Regulator (LQR) to optimize the pure tracking to ensure high precision curved path tracking for curved tracking, finally perform a comparative simulation analysis in MATLAB R2024a. The structural analysis shows that the maximum equivalent stress is 196 MPa and the maximum deformation is 1.73 mm under a load of 600 kg, which are within the yield limit of 45 steel. Simulation results demonstrate that at a speed of 2 m/s, the conventional Pure Pursuit algorithm incurs a maximum lateral error of 0.3418 m and a heading error of 0.2669 rad under high curvature conditions. By contrast, the LQR–Pure Pursuit algorithm reduces the peak lateral error to 0.0904 m and confines the heading error to approximately 0.0217 rad. Experimental validation yielded an RMSE of 0.018 m for lateral error and 0.016 m for heading error. These findings confirm that the designed robot can sustain its payload under most operating scenarios and that the proposed tracking strategy effectively suppresses deviations and improves path-following accuracy.

Keywords: tracking control; chassis design; greenhouse operating robot; finite element analysis; optimized pure tracking algorithm; human–machine collaborative operation (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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