{"title":"Movement Optimization of Robotic Arm Movement Using Soft Computing","authors":"V. K. Banga","volume":117,"journal":"International Journal of Mechanical and Materials Engineering","pagesStart":1698,"pagesEnd":1703,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10005652","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.<\/p>\r\n","references":"[1]\tFu K.S., Gonzalez R.C., Lee C.S.G., Robotics: Control, Sensing, Vision and Intelligence. McGraw Hill International Editions, 1987.\r\n[2]\tLozano P. and Erez T \u201cA simple motion-planning algorithm for general robot manipulators\u201d IEEE Journal of Robotics and Automation 3(3), 224\u2013238, 1987.\r\n[3]\tCameron S. \u201cObstacle avoidance and path planning\u201d. International Journal of Industrial Robot 21(5), 9-14,1994.\r\n[4]\tAnkit Saxena and Abhinav Saxena, \u201cReview of Soft Computing Techinques used in Robotics Applications\u201d Int. journal of Information and Computation Technology, vol.3 (3), 101-106, 2013. \r\n[5]\tAhuactzin J. M., Gupta K. K, \u201cThe kinematic roadmap: a motion planning based global approach for inverse kinematics of redundant robots\u201d IEEE Transactions on Robotics and Automation, 15(4), 653-669, 1999.\r\n[6]\tPiero P. Bonissone, Yu-To Chen, Kai Goebel, And Pratap S. Khedkar, \u201cHybrid Soft Computing Systems: Industrial and Commercial Applications\u201d Proc. IEEE, Vol. 87(9), Sept. 1999.\r\n[7]\tGalicki M., \u201cTime-optimal controls of kinematically redundant manipulators with geometric constraints\u201d. IEEE Transactions on Robotics and Automation 16(1), 89-93,2000.\r\n[8]\tDeb S.R., \u201cRobotics Technology and Flexible Automation\u201d Tata McGraw Hill, 2002.\r\n[9]\tSchilling R.J., \u201cFundamental of Robotics: Analysis and Control\u201d Prentice Hall, India Pvt. Ltd., 2002.\r\n[10]\tFrank Hoffmann, \u201cAn Overview on Soft Computing in Behavior Based Robotics\u201d 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. \r\n[11]\tDongbing Gu, Huosheng Hu, Jeff Reynolds, Edward Tsang, \u201cGA-based Learning in Behavior Based Robotics\u201d Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 16-20 July 2003.\r\n[12]\tGemeinder M. and Gerke M., GA-based path planning for mobile robot systems employing an active search algorithm\u201d Elsevier Applied Soft Computing3(2), 149-158, 2003.\r\n[13]\tTang Y. and Velez-Diaz D., \u201cRobust fuzzy control of mechanical systems\u201d. IEEE Transactions on Fuzzy Systems 11(3), 411- 418,2003.\r\n[14]\tDusko Katic, Miomir Vukobratovic, \u201cGenetic Algorithms in Robotics\u201d International Series on Microprocessor-Based and Intelligent Systems Engineering Vol. 25, 2003.\r\n[15]\tCraig J.J., \u201cIntroduction to Robotics: Mechanics and control\u201d Pearson Education Asia, 2004.\r\n[16]\tO. Hachour, \u201cThe proposed Fuzzy Logic Navigation approach of Autonomous Mobile robots in unknown environments\u201d International Journal of Mathematical Models and Methods in Applied Sciences, 2009.\r\n[17]\tBanga V K, Kumar R, Singh Y \u201cFuzzy genetic optimal control for robotics system\u201d, International journal of Physical Sciences. 6(2), 204-212,2011.\r\n[18]\tBanga V K, Kumar R, Singh Y \u201cModeling and simulation of robotics arm movement using soft computing\u201d World academy of science, 75, 614-620 2011.\r\n[19]\tJorge Armendariz, Vicentre Parra-Vega, Rodolfo Garcia Rodrigez, Sergio Rosales, \u201cNeuro-fuzzy self-turning of PID control for semiglobal exponential tracking of robot arms\u201d, Elsevier Applied Soft Computing, 25, 139-148, 2014.\r\n[20]\tHaitham EI-Hussieny, Samy F. M. Assal, A.A Abouelsound, Said M. Megahed and tsukasa Ogasawara \u201cIncremental learning of reach-to-grasp behavior: A PSO-based Inverse Optial control approach\u201d, Seventh Int. conference of soft Computing and Pattern Recognition (SoCPaR2015), 129-135, 2015.\r\n[21]\tZhi Liu, Ci Chen, Yun Zhang and C. L. Philip Chen, \u201cCoordinated fuzzy control of robotic arms with actuator nonlinearities and motion constraints\u201d Elsevier Information Sciences, 1-13, 2015.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 117, 2016"}