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Geometric Optimization of a Delta Type Parallel Robot Using Harmony Search Algorithm

Published online by Cambridge University Press:  06 February 2019

Mahmood Mazare  and
Mostafa Taghizadeh
Mahmood Mazare
Affiliation:
School of Mechanical Engineering, Shahid Beheshti University, Tehran, Iran E-mail:m_mazare@sbu.ac.ir
Mostafa Taghizadeh*
Affiliation:
School of Mechanical Engineering, Shahid Beheshti University, Tehran, Iran E-mail:m_mazare@sbu.ac.ir
*
*Corresponding author. E-mail: mo_taghizadeh@sbu.ac.ir
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Summary

This paper aims to provide an optimal design of geometric parameters of a special architecture of the delta parallel mechanism, in order to improve positioning accuracy, workspace size, and kinematic and dynamic performance characteristics. In the studied 3[P2(US)] robot, the radius of both fixed and moving platforms, length of the connecting rods, and installation angle of the actuators of the manipulator are chosen as the decision variables. These parameters are optimized to maximize the weighted objective function, comprising workspace volume, global dexterity, global mass, global error, and global error sensitivity indices. Optimizations are performed employing two distinct algorithms, Genetic and Harmony Search whose results confirm each other. The optimal design of the robot leads to maximum workspace size, high dexterity, and dynamic performance, with a minimum error of the end-effector position in its reachable workspace.

Keywords

Parallel robotOptimizationHarmony Search algorithmAccuracyPerformance indices
Type
Articles
Information
Robotica , Volume 37 , Issue 9 , September 2019 , pp. 1494 - 1512
Copyright
© Cambridge University Press 2019 

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References

Yang, G., Chen, I. M., Lin, W. and Angeles, J., “Singularity analysis of three-legged parallel robots based on passive-joint velocities,” IEEE Trans. Rob. Autom. 17(4), 413–422 (2001).CrossRefGoogle Scholar
Wang, Y., “An incremental method for forward kinematics of parallel manipulators,” Robotics, Automation and Mechatronics, 2006 IEEE Conference on, Bangkok, Thailand (IEEE, 2006, June) pp. 1–5.Google Scholar
Dai, J. S., Huang, Z. and Lipkin, H., “Mobility of overconstrained parallel mechanisms,” J. Mech. Des. 128(1), 220–229 (2006).CrossRefGoogle Scholar
Hervé, J. M. and Sparacino, F., “Structural synthesis of parallel robots generating spatial translation,” Proceedings of the 5th IEEE International Conference on Advanced Robotics, Vienna, Austria (1991, June) pp. 808–813.Google Scholar
Kong, X. and Gosselin, C. M., “Type synthesis of 3-DOF translational parallel manipulators based on screw theory,” J. Mech. Des. 126(1), 83–92 (2004).CrossRefGoogle Scholar
Lee, C. C. and Hervé, J. M., “Translational parallel manipulators with doubly planar limbs,” Mech. Mach. Theory 41(4), 433–455 (2006).CrossRefGoogle Scholar
Ropponen, T. and Arai, T., “Accuracy analysis of a modified Stewart platform manipulator,” Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on, Nagoya, Japan, Japan (IEEE, 1995, May) vol. 1, pp. 521–525.CrossRefGoogle Scholar
Ryu, J. and Cha, J., “Volumetric error analysis and architecture optimization for accuracy of HexaSlide type parallel manipulators,” Mech. Mach. Theory 38(3), 227–240 (2003).CrossRefGoogle Scholar
Mazare, M., Taghizadeh, M. and Najafi, M. R., “Kinematic analysis and design of a novel 3-DOF translational parallel robot,” Int. J. Autom. Comput. 14(4), 432–441 (2016).CrossRefGoogle Scholar
Russo, M., Herrero, S., Altuzarra, O. and Ceccarelli, M., “Kinematic analysis and multi-objective optimization of a 3-UPR parallel mechanism for a robotic leg,” Mech. Mach. Theory 120, 192–202 (2018).CrossRefGoogle Scholar
Wu, G., Bai, S. and Hjørnet, P., “Architecture optimization of a parallel Schönflies-motion robot for pickand- place applications in a predefined workspace,” Mech. Mach. Theory 106, 148–165 (2016).CrossRefGoogle Scholar
Zhang, D., Xu, Z., Mechefske, C. M. and Xi, F., “Optimum design of parallel kinematic tool heads with genetic algorithms,” Robotica 22(1), 77–84 (2004).CrossRefGoogle Scholar
Geem, Z. W., Kim, J. H. and Loganathan, G. V., “A new heuristic optimization algorithm: Harmony search,” Simulation 76(2), 60–68 (2001).CrossRefGoogle Scholar
Askarzadeh, A. and Zebarjadi, M., “Wind power modeling using harmony search with a novel parameter setting approach,” J. Wind Eng. Ind. Aerodyn. 135, 70–75 (2014).CrossRefGoogle Scholar
Maleki, A. and Pourfayaz, F., “Sizing of stand-alone photovoltaic/wind/diesel system with battery and fuel cell storage devices by harmony search algorithm,” J. Energy Storage 2, 30–42 (2015).CrossRefGoogle Scholar
Geem, Z.W. and Yoon, Y., “Harmony search optimization of renewable energy charging with energy storage system,” Int. J. Electr. Power Energy Syst. 86, 120–126 (2017).CrossRefGoogle Scholar
Zheng, Y. J., Zhang, M. X. and Zhang, B., “Biogeographic harmony search for emergency air transportation,” Soft Comput. 20(3), 967–977 (2016).CrossRefGoogle Scholar
Hosseini, S. D., Shirazi, M. A. and Ghomi, S. M. T. F., “Harmony search optimization algorithm for a novel transportation problem in a consolidation network,” Eng. Optim. 46(11), 1538–1552 (2014).CrossRefGoogle Scholar
Li, X., Qin, K., Zeng, B., Gao, L. and Su, J., “Assembly sequence planning based on an improved harmony search algorithm,” Int. J. Adv. Manuf. Technol. 84(9–12), 2367–2380 (2016).CrossRefGoogle Scholar
Zeng, B. and Dong, Y., “An improved harmony search based energy-efficient routing algorithm for wireless sensor networks,” Appl. Soft Comput. 41, 135–147 (2016).CrossRefGoogle Scholar
Lee, K. S. and Geem, Z.W., “A new structural optimization method based on the harmony search algorithm,” Comput. Struct. 82(9–10), 781–798 (2004).CrossRefGoogle Scholar
Vasebi, A., Fesanghary, M. and Bathaee, S.M. T., “Combined heat and power economic dispatch by harmony search algorithm,” Int. J. Electr. Power Energy Syst. 29(10), 713–719 (2007).CrossRefGoogle Scholar
Mazare, M., Taghizadeh, M. and Najafi, M. R., “Contouring control of a 3-[P2 (US)] parallel manipulator,” Adv. Rob. 31(9), 496–508 (2017).CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGAII,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).CrossRefGoogle Scholar