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Research on robot motion control and trajectory tracking based on agricultural seeding
L.L. Chen 1

1 Mechanical and Electronic Engineering Department, Weihai Ocean Vocational College,
Rongcheng 264300, Shandong, China

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DOI: 10.18287/2412-6179-CO-1171

Страницы: 179-184.

Язык статьи: English.

Аннотация:
With the development of science and technology, agricultural production has been gradually industrialized, and the use of robots instead of humans for seeding is one of the agricultural industrializations. This paper studied the seeding path planning and path tracking algorithms of the seeding robot, carried out experiments, and compared the improved proportion, integral, differential (PID) algorithm with the traditional PID control algorithm. The results demonstrated that both the improved and non-improved control algorithms played a good role in tracking on the straight path, but the improved control algorithm had a better tracking effect on the turning path; the displacement deviation and angle deviation of the tracking trajectory of the improved PID algorithm were reduced faster and more stable than the traditional PID algorithm; the tracking trajectory was shorter and the operation time of the robot was less under the improved PID algorithm than the traditional one.

Ключевые слова:
seeding robot, trajectory tracking, PID, motion control.

Citation:
Chen LL. Research on robot motion control and trajectory tracking based on agricultural seeding. Computer Optics 2023; 47(1): 179-184. DOI: 10.18287/2412-6179-CO-1171.

References:
  1. Scaglia G, Serrano E, Rosales A, Albertos P. Linear interpolation based controller design for trajectory tracking under uncertainties: Application to mobile robots. Control Eng Pract 2015; 45: 123-132.
  2. Guo J, Li K, Luo Y. Coordinated control of autonomous four wheel drive electric vehicles for platooning and trajectory tracking using a hierarchical architecture. J Dyn Syst Meas Control 2015; 137(10): 1-18.
  3. Li Z, Deng J, Lu R, Xu Y, Bai J, Su C. Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach. IEEE Trans Syst Man Cybern Syst 2017; 46(6): 740-749.
  4. Rijnen M, Saccon A, Nijmeijer H. On optimal trajectory tracking for mechanical systems with unilateral constraints. 2015 54th IEEE Conf on Decision and Control (CDC) 2015: 2561-2566.
  5. Yu C, Chen X. Trajectory tracking of wheeled mobile robot by adopting iterative learning control with predictive, current, and past learning items. Robotica 2015; 33(7): 1393-1414.
  6. Klauer C, Schwabe M, Mobalegh H. Path tracking control for urban autonomous driving. IFAC-PapersOnLine 2020; 53(2): 15705-15712.
  7. Ostafew CJ, Collier J, Schoellig AP, Barfoot TD. Learning-based nonlinear model predictive control to improve vision-based mobile robot path tracking. J Field Robot 2016; 33(1): 133-152.
  8. Oberherber M, Gattringer H, A Müller. Successive dynamic programming and subsequent spline optimization for smooth time optimal robot path tracking. Mech Sci 2015; 6(2): 245-254.
  9. Zhao YM, Lin Y, Xi F, Guo S. Calibration-based iterative learning control for path tracking of industrial robots. IEEE Trans Ind Electron 2015; 62(5): 2921-2929.
  10. Chen X, Watanabe K, Kiguchi K, Izumi K. Translational crawl and path tracking of a quadruped robot. J Robot Syst 2016; 19(12): 569-584.
  11. Kapania NR, Gerdes JC. Path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control. American Control Conf 2015: 2753-2758.
  12. Zhao Y, Zhou F, Li Y, Wang Y. A novel iterative learning path-tracking control for nonholonomic mobile robots against initial shifts. Int J Adv Robot Syst 2017; 14(3). DOI: 10.1177/1729881417710634.
  13. Wang K, Liu Y, Li L. Visual servoing trajectory tracking of nonholonomic mobile robots without direct position measurement. IEEE Trans Robot 2017; 30(4):1026-1035.
  14. Wang N, Qian C, Sun J, Liu Y. Adaptive robust finite-time trajectory tracking control of fully actuated marine surface vehicles. IEEE Trans Control Syst Technol 2016; 24(4): 1454-1462.
  15. Zhang L, Chen W, Liu J, Wen C. A robust adaptive iterative learning control for trajectory tracking of permanent-magnet spherical actuator. IEEE Trans Ind Electron 2015; 63(1): 291-301.
  16. Aguilar-Avelar C, Moreno-Valenzuela J. A composite controller for trajectory tracking applied to the Furuta pendulum. ISA Trans 2015; 57: 286-294.
  17. Jian X, Man W, Lei Q. Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles. Ocean Eng 2015; 105: 54-63.
  18. Kayacan E, Kayacan E, Ramon H, Saeys W. Towards agrobots: Identification of the yaw dynamics and trajectory tracking of an autonomous tractor. Comput Electron Agric 2015; 115: 78-87.
  19. Zhang CL, Li JM. Adaptive iterative learning control of non-uniform trajectory tracking for strict feedback nonlinear time-varying systems with unknown control direction. Appl Math Model 2015; 39(10-11): 2942-2950.
  20. Radac MB, Precup RE, Petriu EM. Model-free primitive-based iterative learning control approach to trajectory tracking of MIMO systems with experimental validation. IEEE Trans Neural Netw Learn Syst 2017; 26(11): 2925-2938.
  21. Naldi R, Furci M, Sanfelice RG, Marconi L. Robust global trajectory tracking for underactuated VTOL aerial vehicles using inner-outer loop control paradigms. IEEE Trans Automat Contr 2016; 62(1): 97-112.

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