Bionic Perception Method of Navel Orange Plucking Position Based on Fmincon and PD Angle Control

Document Type : Original Article


1 School of Information and Communication, Guilin University of Electronic Technology, Guilin, China

2 School of Information and Communication Engineering, Hezhou University, Hezhou, China


In this paper, a bionic perception method of navel orange plucking position based on Fmincon and Proportional Differential (PD) angle control is proposed to solve the problems of wind disturbance and green branches in dynamic unstructured environment. Different from these algorithms that limited to two-dimensional images, this method realizes picking position perception in three-dimensional. Meanwhile, the perception method and the picking robot control algorithm are achieved simultaneously. Firstly, an optimal solution model of the global target rotation angle of the control system based on Fmincon is established to solve the angle optimization problem of robot target approach motion. Secondly, a bionic perception system of plucking position based on PD angle control is constructed to solve specific perception problems. Finally, a joint simulation platform for picking robots based on Solidworks, Adams, and Simulink is given; the validity and accuracy of the algorithm were verified. The experimental results show that the picking accuracy rate is 95%, the angle error of each mechanism and the displacement error are less than 0.5 degrees and 10mm, respectively. The total time from the optimized angle calculation to the system's stability is only about 0.33s. This method is suitable for the rapid perception of plucking position and active angle control of picking robots under dynamic unstructured environment.


Main Subjects

  1. Klerkx, L., Jakku, E., and Labarthe, P. “A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda”, NJAS-Wageningen Journal of Life Sciences, Vol. 2019, No. 90, (2019), 100315. doi: 10.1016/j.njas.2019.100315.
  2. Vasconez, J. P., Kantor, G. A., and Cheein, F. A., A. “Human–robot interaction in agriculture: A survey and current challenges.” Biosystems Engineering, Vol. 2019, No. 179, (2019), 35-48. doi: 10.1016/j.biosystemseng.2018.12.005.
  3. Fountas, S., Espejo-García, B., Kasimati, A., Mylonas, N., and Darra N. “The Future of Digital Agriculture: Technologies and Opportunities.” IT Professional, Vol. 22, No. 1, (2020), 24-28. doi: 10.1109/MITP.2019.2963412.
  4. Bhimanpallewar, R. N., and Narasingarao, M. R. “AgriRobot: implementation and evaluation of an automatic robot for seeding and fertiliser microdosing in precision agriculture.” International Journal of Agricultural Resources, Governance and Ecology, Vol. 16, No. 1, (2020), 33-50.
  5. Moundekar, D., Nakhate, P., Ghosh, S., and Kasetwar, A. R. “Agricultural Robot (Agribot): A Future of Agriculture.” Agriculture International, Vol. 6, No. 04, (2020), 06-10.
  6. Leitner, J. “Picking the right robotics challenge.” Nature Machine Intelligence, Vol. 1, No. 3, (2019), 162-162. doi: 10.1038/s42256-019-0031-6.
  7. Xiong J., Liu, Z., Lin, R., Bu, R., He, Z., Yang, Z., and Liang, C. “Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination.” Sensors, Vol. 18, No. 4, (2018), 969-985. doi:10.3390/s18030969.
  8. Zhuang, J., Hou, C., Tang, Y., He, Y., Guo, Q., Zhong, Z., and Luo, S. “Computer vision-based localisation of picking points for automatic litchi harvesting applications towards natural scenarios.” Biosystems Engineering, Vol. 187, (2019), 1-20. doi: 10.1016/j.biosystemseng.2019.08.016.
  9. Ma, Y., Zhang, W., and Qureshi, W. S. “Autonomous Navigation for a Wolfberry picking robot Using Visual Cues and Fuzzy control.” Information Processing in Agriculture, (2020), doi: 10.1016/j.inpa.2020.04.005.
  10. Yu, Y., Zhang, K., Liu, H., Yang, L., and Zhang, D. “Real-Time Visual Localization of the Picking Points for a Ridge-Planting Strawberry Harvesting Robot.” IEEE Access, Vol. 2020, No. 8, (2020), 116556-116568. doi: 10.1109/ACCESS.2020.3003034.
  11. Lei, W., and Lu, J. “Visual positioning method for picking point of grape picking robot (in Chinese).” Jiangsu Journal of Agricultural Sciences, Vol. 36, No. 4, (2020), 1015-1021. doi: 10.3969/j. issn.1000-4440.2020.04.029.
  12. Wu, B., Akinola, I., Gupta, A., Xu, F., Varley, J., Watkins-Valls, D., and Allen, P. K. “Generative Attention Learning: a GenerAL framework for high-performance multi-fingered grasping in clutter.” Autonomous Robots, Vol. 2020, No. 44, (2020), 971-990. doi: 10.1007/s10514-020-09907-y.
  13. Esfandian, N., and Hosseinpour, K. "A clustering-based approach for features extraction in spectro-temporal domain using artificial neural network." International Journal of Engineering, Transactions B: Applications, Vol. 34, No. 2, (2021), 452-457. doi: 10.5829/IJE.2021.34.02B.17
  14. Siddharth, D., Saini, D. K. J., and Singh, P. "An Efficient Approach for Edge Detection Technique Using Kalman Filter with Artificial Neural Network." International Journal of Engineering, Transactions C: Aspects, Vol. 34, No. 12, (2021), 2604-2610. doi: 10.5829/ije.2021.34.12c.04
  15. Lin, C. Y., Gussu, T. W., and Tsai, Y. N. “A multi objective genetic algorithm approach to a design parameter generation for a robot platform on three omnidirectional wheels.” Journal of the Chinese Institute of Engineers, Vol. 40, No. 8, (2017), 659-668. doi: 10.1080/02533839.2017.1384327.
  16. Wong, C. C., Feng, H. M., Lai, Y. C., and Yu, C. J. “Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks.” Journal of Intelligent & Fuzzy Systems, Vol. 36, No. 2, (2019), 1083-1098. doi: 10.3233/JIFS-169883.
  17. Karami, M., Tavakolpour-Saleh, A. R., and Norouzi, A. “Optimal Nonlinear PID Control of a Micro-Robot Equipped with Vibratory Actuator Using Ant Colony Algorithm: Simulation and Experiment.” Journal of Intelligent & Robotic Systems, 2020, No. 99, (2020), 1-24. doi: 10.1007/s10846-020-01165-5.
  18. Yu, Y., Zhang, K., Yang, L., and Zhang, D. “Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN.” Computers and Electronics in Agriculture, Vol. 2019, No. 163, (2019), 104846. doi: 10.1016/j.compag.2019.06.001.
  19. Shahbakhsh, Mostafa Balouchzehi, and Hamid Hassanpour. "Empowering Face Recognition Methods Using a GAN-based Single Image Super-Resolution Network." International Journal of Engineering, Transactions A: Basics, Vol. 35, No. 10, (2022). doi: 10.5829/ije.2022.35.10a.05
  20. Wang, W., and Fu B. “Target Recognition Method of Eggplant’s Picking Robot under Natural Environment.” Journal of Anhui Agricultural Sciences, 2019, No. 18, (2019), 64.
  21. Chen, W., Xu, T., Liu, J., Wang, M., and Zhao, D. “Picking robot visual servo control based on modified fuzzy neural network sliding mode algorithms.” Electronics, 8, No. 6, (2019), 605. doi: 10.3390/electronics8060605.
  22. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., Y., and Berg, A., C. “SSD: Single shot multibox detector.” European Conference on Computer Vision, Cham, September, (2016), 21-37. doi: 10.1007/978-3-319-46448-0_2.
  23. Jin, Z., Sun, W., Zhang, J., Shen, C., Zhang, H., and Han, S. “Intelligent Tomato Picking Robot System Based on Multimodal Depth Feature Analysis Method.” E&ES, 440, No. 4, (2020), 042074. doi: 10.1088/1755-1315/440/4/042074.
  24. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. “MobileNets: Efficient convolutional neural networks for mobile vision applications.” arXiv preprint, (2017), 1704.04861.
  25. Zhang, X., Zhou, X., Lin, M., and Sun, J. “Shufflenet: An extremely efficient convolutional neural network for mobile devices.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), 6848-6856.
  26. Bloch, V., Bechar, A., and Degani, A. “Development of an environment characterization methodology for optimal design of an agricultural robot Industrial Robot.” An International Journal, 44, No. 1, (2017), 94-103. doi: 10.1108/IR-03-2016-0113.
  27. Taheri, E. "Any-time randomized kinodynamic path planning algorithm in dynamic environments with application to quadrotor." International Journal of Engineering, Transactions A: Basics, 34, No. 10, (2021), 2360-2370. doi: 10.5829/ije.2021.34.10a.17
  28. Kumar, P., and Chaudhary, S., K. "Stability and robust performance analysis of fractional order controller over conventional controller design." International Journal of Engineering, Transactions B: Applications, Vol. 31, No.2, (2018), 322-330. doi: 10.5829/ije.2018.31.02b.17