Movement Optimization of Robotic Arm Movement Using Soft Computing

Authors: V. K. Banga

Abstract:

Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: Artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127140

References:

[1] Fu K.S., Gonzalez R.C., Lee C.S.G., Robotics: Control, Sensing, Vision and Intelligence. McGraw Hill International Editions, 1987.
[2] Lozano P. and Erez T “A simple motion-planning algorithm for general robot manipulators” IEEE Journal of Robotics and Automation 3(3), 224–238, 1987.
[3] Cameron S. “Obstacle avoidance and path planning”. International Journal of Industrial Robot 21(5), 9-14,1994.
[4] Ankit Saxena and Abhinav Saxena, “Review of Soft Computing Techinques used in Robotics Applications” Int. journal of Information and Computation Technology, vol.3 (3), 101-106, 2013.
[5] Ahuactzin J. M., Gupta K. K, “The kinematic roadmap: a motion planning based global approach for inverse kinematics of redundant robots” IEEE Transactions on Robotics and Automation, 15(4), 653-669, 1999.
[6] Piero P. Bonissone, Yu-To Chen, Kai Goebel, And Pratap S. Khedkar, “Hybrid Soft Computing Systems: Industrial and Commercial Applications” Proc. IEEE, Vol. 87(9), Sept. 1999.
[7] Galicki M., “Time-optimal controls of kinematically redundant manipulators with geometric constraints”. IEEE Transactions on Robotics and Automation 16(1), 89-93,2000.
[8] Deb S.R., “Robotics Technology and Flexible Automation” Tata McGraw Hill, 2002.
[9] Schilling R.J., “Fundamental of Robotics: Analysis and Control” Prentice Hall, India Pvt. Ltd., 2002.
[10] Frank Hoffmann, “An Overview on Soft Computing in Behavior Based Robotics” Proc. IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems, Berlin, Heidelberg, pp 544-551, 2003.
[11] Dongbing Gu, Huosheng Hu, Jeff Reynolds, Edward Tsang, “GA-based Learning in Behavior Based Robotics” Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 16-20 July 2003.
[12] Gemeinder M. and Gerke M., GA-based path planning for mobile robot systems employing an active search algorithm” Elsevier Applied Soft Computing3(2), 149-158, 2003.
[13] Tang Y. and Velez-Diaz D., “Robust fuzzy control of mechanical systems”. IEEE Transactions on Fuzzy Systems 11(3), 411- 418,2003.
[14] Dusko Katic, Miomir Vukobratovic, “Genetic Algorithms in Robotics” International Series on Microprocessor-Based and Intelligent Systems Engineering Vol. 25, 2003.
[15] Craig J.J., “Introduction to Robotics: Mechanics and control” Pearson Education Asia, 2004.
[16] O. Hachour, “The proposed Fuzzy Logic Navigation approach of Autonomous Mobile robots in unknown environments” International Journal of Mathematical Models and Methods in Applied Sciences, 2009.
[17] Banga V K, Kumar R, Singh Y “Fuzzy genetic optimal control for robotics system”, International journal of Physical Sciences. 6(2), 204-212,2011.
[18] Banga V K, Kumar R, Singh Y “Modeling and simulation of robotics arm movement using soft computing” World academy of science, 75, 614-620 2011.
[19] Jorge Armendariz, Vicentre Parra-Vega, Rodolfo Garcia Rodrigez, Sergio Rosales, “Neuro-fuzzy self-turning of PID control for semiglobal exponential tracking of robot arms”, Elsevier Applied Soft Computing, 25, 139-148, 2014.
[20] Haitham EI-Hussieny, Samy F. M. Assal, A.A Abouelsound, Said M. Megahed and tsukasa Ogasawara “Incremental learning of reach-to-grasp behavior: A PSO-based Inverse Optial control approach”, Seventh Int. conference of soft Computing and Pattern Recognition (SoCPaR2015), 129-135, 2015.
[21] Zhi Liu, Ci Chen, Yun Zhang and C. L. Philip Chen, “Coordinated fuzzy control of robotic arms with actuator nonlinearities and motion constraints” Elsevier Information Sciences, 1-13, 2015.