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CSME 2019/06
Volume 40 No.3
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239-247
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The Evaluation of a Large-scale Optimization Model for Defect-free RSSR-SS Motion Generation
Wen-Tzong Leea and Kevin Russellb
aDepartment of Biomechatronics Engineering, National Pintung University of Science and Technology, Pingtung, 91201, Taiwan.
bDepartment of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, New Jersey 07102, USA
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Abstract:
The Revolute-Spherical-Spherical-Revolute- Spherical-Spherical joint or RSSR-SS linkage is one of the most basic spatial multi-loop linkages in terms of its construction and its kinematics. In the authors’ original work, a small-scale optimization model was presented and demonstrated for defect-free RSSR-SS linkage motion generation. By small-scale, we mean that the optimization model does not incorporate a general RSSR-SS kinematic displacement model and therefore, no function to explicitly minimize precision position error. In this work, a general RSSR-SS displacement model is fully incorporated in an optimization model to produce, for the first time, a large-scale optimization model with explicit precision position error minimization. This optimization model also includes constraints to eliminate order, branch and circuit defects-defects that are often encountered in classical dyad-based motion generation. With this large-scale optimization model, the dimensions of defect-free RSSR-SS linkages required to approximate precision positions with minimum error are calculated. Therefore, the novelty of this work is the first-time development of an RSSR-SS motion generation model with a minimum error function that simultaneously considers order, branch and circuit defect elimination. In addition to presenting and demonstrating the large-scale optimization model, this work also conveys both the benefits and drawbacks realized when implementing the RSSR-SS optimization model on a personal computer using the commercial mathematical analysis software package Matlab.
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Keywords: RSSR-SS Linkage, Motion Generation, Constrained Nonlinear Optimization, Matlab
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©
2019
CSME , ISSN 0257-9731
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