The Design and Experimentation for Robot Manipulator by Multi-Objective Optimization Algorithm
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Abstract
This research proposes an optimal path design for robot manipulator in picking up and holding things in the industry for a long time. Two objective functions are employed; 1) minimal jerk and 2) the minimum error oscillating of the object. The multi-objective optimization was used solving robot manipulator by comparing two algorithms: Multi-objective Whale optimization Algorithm (MOWOA), Multi-objective Grey Wolf Optimizer (MOGWO), Multi-objective Harmony Search Optimization (MOHS). The performance comparison was made based on the hypervolume (HV) indicator. MOWOA is superior to the other (HV : 192,877.48) and Experimentation in RS020N of Kawasaki Robot. It can reduce the jerk 32.33%.
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References
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