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Application of Meta-Heuristic Optimization Techniques for Design Optimization of a Robotic Gripper

Application of Meta-Heuristic Optimization Techniques for Design Optimization of a Robotic Gripper

Golak Bihari Mahanta, Amruta Rout, Deepak BBVL, Bibhuti Bhusan Biswal
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 27
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522566083|DOI: 10.4018/IJAMC.2019070106
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MLA

Mahanta, Golak Bihari, et al. "Application of Meta-Heuristic Optimization Techniques for Design Optimization of a Robotic Gripper." IJAMC vol.10, no.3 2019: pp.107-133. http://doi.org/10.4018/IJAMC.2019070106

APA

Mahanta, G. B., Rout, A., Deepak BBVL, & Biswal, B. B. (2019). Application of Meta-Heuristic Optimization Techniques for Design Optimization of a Robotic Gripper. International Journal of Applied Metaheuristic Computing (IJAMC), 10(3), 107-133. http://doi.org/10.4018/IJAMC.2019070106

Chicago

Mahanta, Golak Bihari, et al. "Application of Meta-Heuristic Optimization Techniques for Design Optimization of a Robotic Gripper," International Journal of Applied Metaheuristic Computing (IJAMC) 10, no.3: 107-133. http://doi.org/10.4018/IJAMC.2019070106

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Abstract

Robotic grippers play a key player in the industrial robotics application such as pick and place, and assembly. In this article, geometric modeling of a robotic gripper is proposed and a plan is outlined to obtain optimized design parameters of the robotic gripper using various meta-heuristics techniques. The proposed system was solved in two-step methodology as geometric modeling followed by the formulation of objective functions. The developed two objective functions of the robotic gripper are complex and act as the multi-objective constraint optimization problem. Seven decision variables are chosen to develop the geometric model, and the proposed objective function for the robotic gripper is solved using different metaheuristic techniques such as ABC, FA, TLBO, ACO, and PSO algorithm. A statistical study conducted considering the 100 independent run for all the algorithms.

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