CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm

by Akash Dutt Dubey, R. B. Mishra, A. K. Jha
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 13
Year of Publication: 2012
Authors: Akash Dutt Dubey, R. B. Mishra, A. K. Jha
10.5120/8103-1699

Akash Dutt Dubey, R. B. Mishra, A. K. Jha . Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm. International Journal of Computer Applications. 51, 13 ( August 2012), 26-33. DOI=10.5120/8103-1699

@article{ 10.5120/8103-1699,
author = { Akash Dutt Dubey, R. B. Mishra, A. K. Jha },
title = { Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 13 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number13/8103-1699/ },
doi = { 10.5120/8103-1699 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:50:18.887393+05:30
%A Akash Dutt Dubey
%A R. B. Mishra
%A A. K. Jha
%T Task Time Optimization of a Robot Manipulator using Artificial Neural Network and Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 13
%P 26-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we have proposed an evolutionary method to optimize the task time of robot manipulators. Tasks can be planned in joint space with respect to robot joints or in Cartesian space with respect to robot end effector under kinodynamic constraints. Genetic algorithm is implemented to optimize the parameters associated with the selected motion trajectory profile. These optimized results were then taken as the training data to train an artificial neural network which is used to obtain task time, velocity, accelerations and torques required by each motor to perform a given task. The method adopted in this study can be applied to any serial redundant or non-redundant manipulator that has rigid links and known kinematic and dynamic models with free motions or motions along specified paths with obstacle avoidance. The robot kinematic and dynamic models and the optimization method are developed in MATLAB.

References
  1. Angeles J. 2003. Fundamentals of Robotic Mechanical Systems, Theory, Methods, and Algorithms, 2nd ed. , Springer-Verlag Inc.
  2. Kahn, E. and Roth, B. The near-minimum-time control of open-loop articulated kinematic chains. ASME Journal of Dynamic Systems, Measurements, and Control, 1971, 39(3) pp. 164-172.
  3. Bobrow, J. E. and Dubowsky, S. , J. S. Gibson. Time-optimal control of robotic manipulators along specified paths, International Journal of Robotics Research, 1985, 4(3) pp. 3-17.
  4. Shin, K. G. and McKay, N. D. Minimum-Time Control of Robotic Manipulators with Geometric Path Constraints. IEEE Transactions on Automatic Control, 1985, 30(6): Pg. 531-541.
  5. Pfeiffer, F. and R. Johanni, A concept for manipulator trajectory planning, 1985, IEEE Journal of Robotics and Automation, 3(2), pp. 115-123.
  6. Shiller Z. . On singular time-optimal control along specified paths. IEEE Transactions on Robotics and Automation, 1994, 10(4), pp. 561-566.
  7. Shiller, Z. and Dubowsky, S. On computing the global time-optimal motions of robotic manipulators in the presence of obstacles. IEEE Transactions on Robotics and Automation, 1991, 7(6), pp. 785-797.
  8. Chen, Y. and Desrochers. A. A. 1989. Structure of minimum-time control law for robotic manipulators with constrained paths, In Proceedings of 1989 IEEE International Conference on Robotics and Automation, Scottsdale, AZ, pp. 971-976.
  9. Sontag, E. D. and Sussmann, H. J. 1985. Remarks on the time-optimal control of two-link manipulators. In Proceedings of 24th IEEE International Conference on Decision and Control, Ft. Lauderdale, F L, pp. 1646-1652.
  10. Sontag E. D. and Sussmann, H. J. 1986. Time-optimal control of manipulators, In: Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, CA, pp. 1692-1697.
  11. Fourquet, J. Y. 1993. Optimal control theory and complexity of the time optimal problem for rigid manipulators. In Proceedings of the 1993IEEE/YRSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan.
  12. Siciliano, B. , Scilavicco, L. , Villani, L. and Oriolo, G. 2009. Robotics Modeling, Planning and Control, Springer-Verlag Inc.
  13. Haddad, M. , Chettibi, T. , Khalil, W. and Lehtihet, H. 2007. Trajectory Generation, in Robot Manipulators, Modeling, Performance, Analysis and Control, W. Khalil and E. Dombre, Eds, 1st ed. , London: ISTE Ltd.
  14. Pettersson, M. and Olvander J. 2007. Adaptive complex method for efficient design optimization. In Proceedings of ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Las Vegas, Nevada, USA, vol. 6: 33rd Design Automation Conference, Parts A and B, pp. 265-272.
  15. Stryk, O. V. and Buliesch, R. Direct and indirect methods for trajectory optimization, Annals of Operations Research, 1993, 37(1), pp. 357-373.
  16. Bobrow, J. E. , Martin, B. J. , Sohi, G. , Wang, E. C. , Park, F. C. and Kim, J. Optimal robot motions for physical criteria, Journal of Robotic Systems, 2001, 18(12), pp. 785-795.
  17. Rana, A. S. and Zalazala A. M. S. Near time optimal collision free motion planning of robotic manipulators using an evolutionary algorithm, Robotica, 1996, 14, pp. 621-632.
  18. Hatem A. Al-Dois, Jha, A. K. and Mishra, R. B. Task-based design optimization of serial robot manipulators. Engineering Optimization. August, 2012.
  19. Deb, K. 2001. Multi-objective Objective Optimization using Evolutionary algorithms, Wiley and Sons Ltd (2001)
  20. Parker, J. , Khoogar, K. A. R, and Goldberg, D. E. 1989. Inverse kinematics of redundant robots using genetic algorithms. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 271-276.
  21. Yun, W. M. and Xi, Y. G. Optimum motion planning in joint space for robots using genetic algorithms, Robotics and Autonomous Systems, 1996, 18(4), pp. 373-393.
  22. Hirakawa, A. R. and Kawamura, A. Trajectory generation for redundant manipulators under optimization of consumed electrical energy. In the 31st IAS Annual Meeting IAS 96, 1996, vol 3, pp. 1626-1632.
  23. McAvoy, B. , Sangolola, B. and Szabad, Z. Optimal trajectory generation for redundant planar manipulator. In proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2000, Nashville, pp. 3241-3246.
  24. Tian, L. and Collins, C. Motion planning for redundant manipulators using a floating point genetic algorithm. Journal of Intelligent and Robotic Systems: Theory and Applications, 2003, 38(3-4), pp. 297-312.
  25. Minsky, M. and Papert S. 1969. Perceptrons. MIT Press.
  26. Werbos, P. Beyond regression: New tools for prediction and analysis in the behavioural sciences. 1974. PhD thesis, Harvard University, Cambridge, MA.
  27. Hopfield, J. J. 1982. Neural networks and physical systems with emergent collective computational properties. In proceedings of the National Academy of Sciences of the USA, 79:2554 – 2588.
  28. Kawato M, Uno Y, Isobe M, Suzuki R. Hierarchical neural network model for voluntary movement with application to robotics. IEEE Control Systems Magazine, 1988, 8, 8-16.
  29. Ciliz, M. K. and Isik, C. Trajectory following control of robotic manipulators using neural networks. In proceedings of the 5th IEEE International Symposium on Intelligent Control, Philadelphia, Pennsylvania, 1990, pp. 536-540.
  30. Šafari?, R. , Jezernik, K. 1994. Trajectory tracking neural network controller for a robot mechanism and Lyapunov theory of stability. Conference Proceedings IROS, p. 626-633.
  31. Zhao, H. , Ishida, T Trajectory tracking control of industrial robot manipulators using a neural network controller. IEEE International Conference on Systems, Man and Cybernetics, 2007, ISIC. 978-1-4244-0991-4: 2390 – 2395.
  32. Dubey, A. D. , Mishra, R. B. Jha, A. K. Design and Path Planning of a Mobile Robot. International Journal pf Computer Science and technology (IJCST), 2012, 3(2), VER 1, 0976-8491, Pg. 73-77.
  33. Denavit, J. and Hartenberg, R. S. A Kinematic notation for lower-pair mechanisms based on matrices. ASME Journal of Applied Mechanisms, 1995, vol 77, pp. 215-221.
Index Terms

Computer Science
Information Sciences

Keywords

Pick and Place Artificial Neural Network Genetic Algorithm Robot Manipulator End effector Mobile robot

Powered by PhDFocusTM