Inverse Kinematics Analysis of Novel 6-DOF Robotic Arm Manipulator for Oil and Gas Welding Using Meta-Heuristic Algorithms
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Abstract
This research presents a comparison of the grey-wolf, improved grey-wolf, particle swarm, jellyfish and whale optimisation algorithms regarding the inverse kinematics solution of a newly designed 6-degrees of freedom robotic arm for oil and gas pipeline welding which has not been used in literature. Consequently, due to the robot’s multiple joints with compounding combinatory possibilities of joint angles, the analysis of the inverse kinematics is relatively complex. In this research, the meta-heuristic algorithms, have been used to determine the robotic arm's inverse kinematics, essential for tracking a rectangular trajectory with six sets of waypoints in the 3D [X, Y, Z] space. The results were further analysed in terms of the accuracy of the position of the end effector from the accurate position of the rectangular target trajectory via a mean squared error cost function. Furthermore, the results of comparison between the meta-heuristic algorithmsto position error from the inverse kinematics task demonstrated the superior performance of the grey-wolf algorithm over the particle swarm, improved grey-wolf, jellyfish, and whale optimisation algorithms.
(Manuscript received: 24 April 2022 | Accepted: 16 June 2022 | Published: 8 July 2022)
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