Skip to main content
Log in

Vision-Based Robotic Motion Control for Non-autonomous Environment

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

A visual servo control system with SOPC structure is implemented on a retrofitted Mitsubishi Movemaster RV-M2 robotic system. The hardware circuit has the functions of quadrature encoder decoding, limit switch detecting, pulse width modulation (PWM) generating and CMOS image signal capturing. The software embedded in Nios II micro processor has the functions of using UART to communicate with PC, robotic inverse kinematics calculation, robotic motion control schemes, digital image processing and gobang game AI algorithms. The digital hardware circuits are designed by using Verilog language, and programs in Nios II micro processor are coded with C language. An Altera Statrix II EP2S60F672C5Es FPGA chip is adopted as the main CPU of the development board. A CMOS color image sensor with 356 ×292 pixels resolution is selected to catch the environment time-varying change for robotic vision-based servo control. The system performance is evaluated by experimental tests. A gobang game is planned to reveal the visual servo robotic motion control objective in non-autonomous environment. Here, a model-free intelligent self-organizing fuzzy control strategy is employed to design the robotic joint controller. A vision based trajectory planning algorithm is designed to calculate the desired angular positions or trajectory on-line of each robotic joint. The experimental results show that this visual servo control robot has reliable control actions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lin, F.J., Wang, D.H., Huang, P.K.: FPGA-based fuzzy sliding-mode control; for a linear induction motor drive. Proc. IEEE Int. Conf. Electrical Power Appl. 152(5), 1137–11148 (2005) Sept

    Article  Google Scholar 

  2. Kung, Y.S., Shu, G.S.: Development of a FPGA-based motion control IC for robot arm. Proc. IEEE Int. Conf. Ind. Technol., pp. 1397–1402, Hong Kong, 14–17 December 2005

  3. Okura, M., Murase, K.: Artificial evolution of FPGA that control a miniature mobile robot Khepera. In: Proceedings of the Autonomous Mini-robots for Research and Edulainmente (AniiRE2003), pp. 103–111 (2003)

  4. Tzafestas, S., Dristsas, L.: Combined computed torque and model reference adaptive control of robot system. J. Franklin Inst. 327(2), 273–294 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  5. Lee, H.-H., Chlick, F.E.: Design of a adaptive control law for robotic manipulator. J. Robot. Syst. 11(4), 241–255 (1994) June

    Article  MATH  Google Scholar 

  6. Dessaint, L.-A., Sand, M., Hebert, B.: An adaptive controller for a direct-drive SCARA Robot. IEEE Trans. Ind. Electron. 39(2), 105–111 (1995) April

    Article  Google Scholar 

  7. Huang, S.-J., Lian, R.-J.: A hybrid fuzzy logic and neural network algorithm for robot motion control. IEEE Trans. Ind. Electron. 44(3), 408–417 (1997)

    Article  Google Scholar 

  8. Huang, S.-J., Lee, J.-S.: A stable self-organizing fuzzy controller for robotic motion control. IEEE Trans. Ind. Electron. 47(2), 421–428 (2000)

    Article  Google Scholar 

  9. Procky, T.J., Mamdani, E.H.: A linguistic self-organizing process controller. Automatica 15, 15–30 (1979)

    Article  Google Scholar 

  10. Zhang, B.S., Edmunds, J.M.: Self-organizing fuzzy logic controller. IEE Proceedings-D 139(5), 460–464 (1992)

    MATH  Google Scholar 

  11. Shao, S.: Fuzzy self-organizing controller and its application for dynamic processes. Fuzzy Sets Syst. 26, 151–164 (1988)

    Article  Google Scholar 

  12. Wakileh, B.A.M., Gill, K.F.: Robot control using self-organizing fuzzy logic. Comput. Ind. 15(3), 175–186 (1990)

    Article  Google Scholar 

  13. Maeda, M., Murakami, S.: A self-tuning fuzzy controller. Fuzzy Sets Syst. 51, 29–40 (1992)

    Article  Google Scholar 

  14. Wu, Z.Q., Wang, P.Z., Teh, H.H.: A rule self-regulating fuzzy controller. Fuzzy Sets Syst. 47, 13–21 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  15. Bauchspiess, A., Absi, A., Sadek, C., Dobrzanski, L.A.: Predictive sensor guided robotic manipulators in automated welding cells. J. Mater. Process. Technol. 109(1–2), 13–19 (2001) 1 February

    Article  Google Scholar 

  16. Xiong, Y., Quek, F.: Machine vision for 3D mechanical part recognition in intelligent manufacturing environments. In: Proceedings of the Third International Workshop on Robot Motion and Control, RoMoCo ‘02, pp. 441–446 (2002)

  17. Lin, C.-S., Lue, L.-W.: An image system for fast positioning and accuracy inspection of ball grid array boards. Microelectron. Reliab. 41(1), 119–128 (2001) January

    Article  Google Scholar 

  18. Xiao, N.-F., Nahavandi, S.: Multi-agent model for robotic assembly system. In: Proceedings of the 5th Biannual World Automation Congress, vol. 14, pp. 495–500 (2002)

  19. Blasco, J., Aleixos, N., Roger, J.M., Rabatel, G., Molto, E.: Automation and emerging technologies: robotic weed control using machine vision. Biosyst. Eng. 83(2), 149–157 (2002) October

    Article  Google Scholar 

  20. Son, C.: Optimal control planning strategies with fuzzy entropy and sensor fusion for robotic part assembly tasks. Int. J. Mach. Tools Manuf. 42(12), 1335–1344 (2002) September

    Article  MathSciNet  Google Scholar 

  21. PAS106BCB283 datasheet, Version 2, Pixart Image Inc. http://www.pixart.com.tw (2002)

  22. Wang, L.T., Chen, C.C.: A combined optimization method for solving the inverse kinematics problem of mechanical manipulator. IEEE Trans. Robot. Autom. 7(4), 489–499 (1991)

    Article  Google Scholar 

  23. Kazerounian, K.: On the numerical inverse kinematics of robotic manipulator. AMSEJ of Mechanisms, Transmissions and Automation in Design, vol. 109, pp. 8–13 (1987) March

  24. Yang, C.Z.: Design of real-time linguistic self-organizing fuzzy controller. Master thesis, Department of Mechanical Engineering, National Taiwan University (1992)

  25. Narendra, K.S., Annaswamy, A.M.: A new adaptive law for robust adaptation without persistent excitation. IEEE Trans. Automat. Contr. AC-32(2), 134–145 (1987)

    Article  MathSciNet  Google Scholar 

  26. Chen, F.C., Khalil, H.K.: Adaptive control of nonlinear systems using neural networks a dead zone approach. In: Proceedings of the 1991 American Control Conference, pp. 667–672 (1991)

  27. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing using MATLAB. Pearson, Prentice Hall, Upper Saddle River, NJ (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shiuh-Jer Huang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, SJ., Wu, SS. Vision-Based Robotic Motion Control for Non-autonomous Environment. J Intell Robot Syst 54, 733–754 (2009). https://doi.org/10.1007/s10846-008-9286-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-008-9286-6

Keywords

Navigation