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A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators

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Abstract

In robotics, inverse kinematics problem solution is a fundamental problem in robotics. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention has been focused on a neural-network-based inverse kinematics problem solution in robotics. However, the result obtained from the neural network requires to be improved for some sensitive tasks. In this paper, a neural-network committee machine (NNCM) was designed to solve the inverse kinematics of a 6-DOF redundant robotic manipulator to improve the precision of the solution. Ten neural networks (NN) were designed to obtain a committee machine to solve the inverse kinematics problem using separately prepared data set since a neural network can give better result than other ones. The data sets for the neural-network training were prepared using prepared simulation software including robot kinematics model. The solution of each neural network was evaluated using direct kinematics equation of the robot to select the best one. As a result, the committee machine implementation increased the performance of the learning.

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References

  1. Abdel-Aal RE (2005) Improving electric load forecasts using network committees. Electr Power Syst Res 74:83–94

    Article  Google Scholar 

  2. Astrom F, Koker R (2011) A parallel neural network approach to prediction of Parkinson’s disease. Expert Syst Appl 38:12470–12474

    Article  Google Scholar 

  3. Bingul Z, Ertunc HM (2005) Applying neural network to inverse kinematics problem for 6R robot manipulator with offset wrist. In: Proceedings of 7th International Conference on Adaptive and Natural Computing Algorithms, Coimbra, Portugal

  4. Chen CH, Lin ZS (2006) A committee machine with empirical formulas for permeability prediction. Comput Geosci 32:485–496

    Article  Google Scholar 

  5. Daunicht W (1991) Approximation of the inverse kinematics of an industrial robot by DEFAnet. In: Proceedings of IEEE International Joint Conference on Neural Networks, Singapore, pp 531–538

  6. Denavit J, Hartenberg R (1955) A kinematic notation for lower-pair mechanisms based on matrices. ASME J Appl Mech 22:215–221

    MathSciNet  MATH  Google Scholar 

  7. Duffy J (1980) Analysis of mechanisms and robot manipulators. Wiley, New York

    Google Scholar 

  8. Dugulena M, Barbuceanu FG, Teirelbar A, Mogan G (2012) Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning. Robotics Comput Integr Manuf 28:132–146

    Google Scholar 

  9. Featherstone R (1983) Position and velocity transformation between robot end-effector coordinate and joint angle. Int J Robotics Res 2(2):33–45

    Article  Google Scholar 

  10. Fernandes AM, Utkin AB, Lavrov AV, Vilar RM (2004) Development of neural network committee machines for automatic forest fire detection using lidar. Pattern Recogn 37:2039–2047

    Article  MATH  Google Scholar 

  11. Fu KS, Gonzalez RC, Lee CSG (1987) Robotics: control, sensing, vision, and intelligence. McGraw-Hill, New York

    Google Scholar 

  12. Hahala J, Fahner G, Echmiller R (1991) Rapid learning of inverse robot kinematics based on connection assignment and topographical encoding (CATE). In: Proceedings of IEEE International Joint Conference on Neural Networks, Singapore, pp 1536–1541

  13. Hasan AT, Hamouda AMS, Ismail N, Al-Assadi HMAA (2006) An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 D.O.F. serial robot manipulator. Adv Eng Softw 37:432–438

    Article  Google Scholar 

  14. Hasan AT, Ismail N, Hamouda AMS, Aris I, Marhaban MH, Al-Assadi HMAA (2010) Artificial neural network-based kinematics Jacobean solution for serial manipulator passing through singular configurations. Adv Eng Softw 41:359–367

    Article  MATH  Google Scholar 

  15. Hirayama V, Fernandez FJR, Salcedo WJ (2006) Committee machine for LPG calorific power classification. Sens Actuators B 116:62–65

    Article  Google Scholar 

  16. Jafari SA, Mashohor S, Varnamkhasti MJ (2011) Committee neural networks with fuzzy genetic algorithm. J Petrol Sci Eng 76:217–223

    Article  Google Scholar 

  17. Karimpouli S, Fathianpour N, Roohi J (2010) A new approach to improve neural networks’ algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN). J Petrol Sci Eng 73:227–232

    Article  Google Scholar 

  18. Karlık B, Aydın S (2000) An improved approach to the solution of inverse kinematics problems for robot manipulators. Eng Appl Artif Intell 13:159–164

    Article  Google Scholar 

  19. Köker R (2005) Reliability-based approach to the inverse kinematics solution of robots using Elman’s network. Eng Appl Artif Intell 18:685–693

    Article  Google Scholar 

  20. Köker R (2012) A genetic algorithm approach to a neural network-based inverse kinematics solution of robotic manipulators based on error minimization. Information sciences, USA

    Google Scholar 

  21. Köker R, Oz C, Cakar T, Ekiz H (2004) A study of neural network based inverse kinematics solution for a three-joint robot. Robotics Auton Syst 49:227–234

    Article  Google Scholar 

  22. Korein JU, Balder NI (1982) Techniques for generating the goal-directed motion of articulated structures. IEEE Comput Graphics Appl 2(9):71–81

    Article  Google Scholar 

  23. Küçük S, Bingül Z (2004) The inverse kinematics solutions of industrial robot manipulators. In: Proceedings of the IEEE International Conference on Mechatronics, Kuşadası-Turkey, pp 274–279

  24. Lee GCS (1982) Robot arm kinematics, dynamics, and control. IEEE Comput 15(12):62–79

    Article  Google Scholar 

  25. Manocha D, Canny JF (1994) Efficient inverse kinematics for general 6R manipulators. IEEE Transact Robotic Autom 10(5):648–657

    Article  Google Scholar 

  26. Martin JAH, Lope J, Santos M (2009) A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers. Neurocomputing 72:2806–2814

    Article  Google Scholar 

  27. Martinetz T, Ritter H, Schulten K (1990) Three-dimensional neural net for learning visuomotor coordination of a robot arm. IEEE Trans Neural Netw 1(1):131–136

    Article  Google Scholar 

  28. Mayorga RV, Sanongboon P (2005) Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: an artificial neural network approach. Robotics Auton Syst 53:164–176

    Article  Google Scholar 

  29. Mi Z, Yang J, Kim JH, Abdel-Malek K (2011) Determining the initial configuration of uninterrupted redundant manipulator trajectories in a manufacturing environment. Robotics Comput Integr Manuf 27:22–32

    Article  Google Scholar 

  30. Oyama E, Chong NY, Agah A (2001) Inverse kinematics learning by modular architecture neural networks with performance prediction networks. In: Proceedings of 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea

  31. Paul RP, Shimano B, Mayer GE (1981) Kinematics control equations for simple manipulators. IEEE Transact Syst Man Cybernetics SMC-11 6:66–72

    Google Scholar 

  32. Takenori K, Dagli C (2009) Hybrid approach to the Japanese candlestick method for financial forecasting. Expert Syst Appl 36:5023–5030

    Article  Google Scholar 

  33. Tejomurtula S, Kak S (1999) Inverse kinematics in robotics using neural networks. Inf Sci 116:147–164

    Article  MathSciNet  MATH  Google Scholar 

  34. Vosniakos GC, Kannas Z (2009) Motion coordination for industrial robotic systems with redundant degrees of freedom. Robotics Comput Integr Manuf 25:417–431

    Article  Google Scholar 

  35. Zhang D, Lei J (2011) Kinematic analysis of a novel 3-DOF actuation parallel manipulator using artificial intelligence approach. Robotics Comput Integr Manuf 27:157–163

    Article  Google Scholar 

  36. Zhao ZQ, Huang DS, Sun BY (2004) Human face recognition based on multi-features using neural networks committee. Pattern Recogn Lett 25:1351–1358

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Electronics and Computer Sciences Department, Technical Education Faculty, Sakarya University, 54187, Sakarya, Turkey

    Raşit Köker

  2. Industrial Engineering Department, Engineering Faculty, Sakarya University, 54187, Sakarya, Turkey

    Tarık Çakar

  3. Electronics and Automation Department, Hendek Vocational High School, Sakarya University, 54300, Hendek, Sakarya, Turkey

    Yavuz Sari

Authors
  1. Raşit Köker
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  2. Tarık Çakar
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  3. Yavuz Sari
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Correspondence to Raşit Köker.

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Köker, R., Çakar, T. & Sari, Y. A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators. Engineering with Computers 30, 641–649 (2014). https://doi.org/10.1007/s00366-013-0313-2

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  • DOI: https://doi.org/10.1007/s00366-013-0313-2

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