Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-28T07:30:01.147Z Has data issue: false hasContentIssue false
  • English
  • Français

Path tracking control method for automatic navigation rice transplanters based on VUFC and improved BAS algorithm

Published online by Cambridge University Press:  10 July 2023

Dequan Zhu ,
Menghao Shi Open the ORCID record for Menghao Shi [Opens in a new window] ,
Yang Wang ,
Kang Xue ,
Juan Liao ,
Wei Xiong ,
Fuming Kuang  and
Shun Zhang
Dequan Zhu*
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery Equipment, Anhui Agricultural University, Hefei, China
Menghao Shi
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China
Yang Wang
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China
Kang Xue
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China
Juan Liao
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China
Wei Xiong
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China
Fuming Kuang
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China
Shun Zhang
Affiliation:
School of Engineering, Anhui Agricultural University, Hefei, P.R. China
*
Corresponding author: Dequan Zhu; Email: zhudequan@ahau.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

During the operation of automatic navigation rice transplanter, the accuracy of path tracking is influenced by whether the transplanter can enter the stable state of linear path tracking quickly, thus affecting the operation quality and efficiency. To reduce the time to enter the path tracking stable state and improve the tracking accuracy and stability for the rice transplanter, path tracking control method based on variable universe fuzzy control (VUFC) and improved beetle antenna search (BAS) is proposed in this paper. VUFC is applied to achieve adaptive adjustment of the fuzzy universe by dynamically adjusting the quantization and scaling factors according to the variations of errors by the contraction–expansion factor. To solve the problem of setting the contraction–expansion factor in VUFC and real-time performance, an offline parameter optimization method is presented to calculate the optimal contraction–expansion factor by an iterative optimization algorithm in a path tracking simulation model, where the iterative optimization algorithm is the BAS algorithm improved by the isolated niching technique and adaptive step size strategy in this paper. To verify the effectiveness of the proposed path tracking control method, simulation and field linear path tracking experiments were carried out. Experimental results indicate that the proposed method reduces the time of entering the stable state of linear path tracking and improves the accuracy and stability of path tracking compared with the pure pursuit control method.

Keywords

path tracking algorithmpure pursuit algorithmrice transplanterthe beetle antennae search algorithmvariable universe fuzzy control
Type
Research Article
Information
Robotica , Volume 41 , Issue 10 , October 2023 , pp. 3116 - 3136
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hu, J., Gao, L. and Bai, X., “Review of research on automatic guidance of agricultural vehicles,” Trans. CSAE 31(10), 1–10 (2015).Google Scholar
Ji, C. and Zhou, J., “Current situation of navigation technologies for agricultural machinery,” Trans. CSAM 45(9), 44–54 (2014).Google Scholar
Wang, H., Wang, G. and Luo, X., “Path tracking control method of agricultural machine navigation based on aiming pursuit model,” Trans. CSAE 35(4), 11–19 (2019).Google Scholar
Zhang, W., Gai, J., Zhang, Z., Tang, L., Liao, Q. and Ding, Y., “Double-DQN based path smoothing and tracking control method for robotic vehicle navigation,” Comput. Electron. Agric. 166, 104985 (2019).CrossRefGoogle Scholar
De, S., Tabile, R., Inamasu, R. and Porto, A., “A row crop following behavior based on primitive fuzzy behaviors for navigation system of agricultural robots,” IFAC-Proc. 46(18), 91–96 (2013).Google Scholar
He, Z., Nie, L., Yin, Z. and Huang, S., “A two-layer controller for lateral path tracking control of autonomous vehicles,” Sensors (Basel) 20(13), 3689 (2020).CrossRefGoogle ScholarPubMed
Moveh, S., Mohamed, H. and Binti, M., “A review of some pure-pursuit based path tracking techniques for control of autonomous vehicle,” Int. J. Comput. Appl. 135(1), 35–38 (2016).Google Scholar
Yin, X., Du, J., Geng, D. and Jin, C., “Development of an automatically guided rice transplanter using RTK-GNSS and IMU,” IFAC-PapersOnLine 51(17), 374–378 (2018).CrossRefGoogle Scholar
Luo, X., Zhang, Z. and Zhao, Z., “Design of DGPS navigation control system for Dongfanghong X-804 tractor,” Trans. CSAE 25(11), 139–145 (2009).Google Scholar
Chen, J., Niao, C. and Zhu, Z., “Study on automatic guidance for tractor on grassland,” Trans CSAM 36(7), 104–107 (2015).Google Scholar
Huang, P., Zhang, Z., Luo, X. and Huang, P., “Path tracking control of a differential-drive tracked robot based on look-ahead distance,” IFAC-PapersOnLine 51(17), 112–117 (2018).CrossRefGoogle Scholar
Mehmet, B., Ehsan, K. and Hasan, H., “Double look-ahead reference point control for autonomous agricultural vehicles,” Biosyst. Eng. 113(2), 173–186 (2012).Google Scholar
Ding, Y., Wang, L., Li, Y. and Li, D., “Model predictive control and its application in agriculture: A review,” Comput. Electron. Agric. 151, 104–117 (2018).CrossRefGoogle Scholar
Hamid, T. and Subhash, R., “Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller,” Mech. Syst. Signal Process. 130, 41–55 (2019).Google Scholar
Li, S., Xu, H., Ji, Y., Cao, R., Zhang, M. and Li, H., “Development of a following agricultural machinery automatic navigation system,” Comput. Electron. Agric. 158, 335–344 (2019).CrossRefGoogle Scholar
Meng, Q., Qiu, R., He, J., Zhang, M., Ma, X. and Liu, G., “Development of agricultural implement system based on machine vision and fuzzy control,” Comput. Electron. Agric. 112, 128–138 (2015).CrossRefGoogle Scholar
Korayem, M. H., Nekoo, S. R. and Kazemi, S., “Finite-time feedback linearization (FTFL) controller considering optimal gains on mobile mechanical manipulators,” J. Intell. Robotic Syst. 94(3–4), 1–18 (2019).Google Scholar
Keymasi Khalaji, S. A. A. M., “Adaptive sliding mode control of a wheeled mobile robot towing a trailer,” Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 229(2), 169–183 (2015).Google Scholar
Mirzaeinejad, H. and Shafei, A. M., “A novel recursive formulation for dynamic modeling and trajectory tracking control of multi-rigid-link robotic manipulators mounted on a mobile platform,” Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 235 (7), 1204–1217 (2021).Google Scholar
Shojaei, K., Shahri, A. R. M. and Tarakameh, A. R., “Adaptive feedback linearizing control of nonholonomic wheeled mobile robots in presence of parametric and nonparametric uncertainties,” Robot. Comput. Integr. Manuf. 27(1), 149–204 (2011).CrossRefGoogle Scholar
Azizi, M. R. and Keighobadi, J., “Point stabilization of nonholonomic spherical mobile robot using nonlinear model predictive control,” Robot. Auton. Syst. 98, 347–359 (2017).CrossRefGoogle Scholar
Keely, A., A Feedforward Control Approach to the Local Navigation Problem for Autonomous Vehicles (Carnegie Mellon University, USA, 1994).CrossRefGoogle Scholar
Liu, J., Li, X. and Zang, Y., “Modelling and experimental study on active energy-regenerative suspension structure with variable universe fuzzy PD control,” Shock Vib. 2016, 1–11 (2016).Google Scholar
Zheng, J., Chen, J. and Quan, O., “Variable universe fuzzy control for battery equalization,” J. Syst. Sci. Complex. 31(1), 325–342 (2018).CrossRefGoogle Scholar
Pang, H., Liu, F. and Xu, Z., “Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization,” Neurocomputing 306, 130–140 (2018).CrossRefGoogle Scholar
Wang, Y., Wei, M., Hu, X., Jang, M. and Zhang, L., “Research on variable universe fuzzy PID control strategy of pipe lining induction heating system,” Model. Simul. Eng. 2020, 1–9 (2020).CrossRefGoogle Scholar
Ma, G., Yu, Z., Cao, G., Zhang, R. and Zheng, Y., “The motion path study of measuring robot based on variable universe fuzzy control,” MATEC Web Conf. 95, 8011 (2017).CrossRefGoogle Scholar
Yao, J., Lu, J. and Zheng, Y., “DC motor speed control of annual-ring measuring instrument based on variable universe fuzzy control algorithm,” Trans. CSAE 35(14), 57–63 (2019).Google Scholar
Jiang, X. and Li, S., “BAS: Beetle antennae search algorithm for optimization problems,” Int. J. Robot. Control 1(1), 1–5 (2018).CrossRefGoogle Scholar
Fan, Y., Shao, J., Sun, G. and Shao, X., “Improved beetle antennae search algorithm-based Lévy flight for tuning of PID controller in force control system,” Math. Probl. Eng. 2020, 1–22 (2020).Google Scholar
Lin, Y., Hao, J. and Ji, Z., “A study of genetic algorithm based on isolation niche technique,” J. Syst. Eng. 15(1), 86–91 (2000).Google Scholar
Li, X.-L., Serra, R., Olivier, J. and Fei, C., “Performance of fitness functions based on natural frequencies in defect detection using the standard PSO-FEM approach,” Shock Vib. 2021(1), 1–9 (2021).Google Scholar
Yin, J., Zhu, D., Liao, J., Liu, L., Wang, Y. and Chu, T., “Path tracking algorithm based on speed-adaptive fuzzy control for rice transplanter,” Int. Agric. Eng. J. 28(4), 1–9 (2019).Google Scholar