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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 algorithms to 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.
M.A.N. Huda, S.H Susilo, and P.M. Adhi, “Implementation of Inverse Kinematic and Trajectory Planning on 6-DOF Robotic Arm for Straight-Flat Welding Movement”, Logic: Jurnal Rancang Bangun dan Teknologi, vol. 22, no. 1, pp.51-61, 2022.
M. Saraf, A. Agarwal, A. Chaudhary, and A. Ganthale, “Kinematic Modelling and Motion Mapping of Robotic Arms,” Journal of Physics: Conference Series, vol. 1969, no. 1, 2021.
M. T. Nguyen, C. Yuan, and J. H. Huang, “Kinematic Analysis of A 6-DOF Robotic Arm,” IFToMM World Congress on Mechanism and Machine Science, vol. 73, no. 100, pp. 2965–2974, 2019.
S. Ondocko, T. Stejskal, J. Svetlík, L. Hrivniak, M. Sasala, and A. Zilinsky, “Position Forward Kinematics of 6-DOF Robotic Arm,” Acta Mech. Slovaca, vol. 24, no. 2, pp. 30–36, 2020.
S. Ondocko et al., “Inverse kinematics data adaptation to non-standard modular robotic arm consisting of unique rotational modules,” Applied Sciences, vol. 11, no. 3, pp. 1–15, 2021.
A. R. Al Tahtawi, M. Agni, and T. D. Hendrawati, “Small-scale robot arm design with pick and place mission based on inverse kinematics,” Journal of Robotics and Control (JRC), vol. 2, no. 6, pp. 469–475, 2021.
A. Saadah, “Computing the Kinematics Study of a 6 DOF Industrial Manipulator Prototype by Matlab,” Recent Innovations in Mechatronics, vol. 7, no. 1., 2021.
S. Dereli and R. Koker, “A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm,” Artificial Intelligence Review, vol. 53, no. 2, pp. 949–964, 2020.
H. Khan, H. H. Kim, S. J. Abbasi, and M. C. Lee, “Real-time inverse kinematics using dual particle swarm optimization DPSO of 6-DOF robot for nuclear plant dismantling,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 9885–9890, 2020.
S. Luo, D. Chu, Q. Li, and Y. He, “Inverse Kinematics Solution of 6-DOF Manipulator Based on Multi-Objective Full-Parameter Optimization PSO Algorithm,” Frontiers in Neurorobotics, vol. 16, pp. 1–12, 2022.
[1 L. I. U. Yiyang, X. I. Jiali, B. A. I. Hongfei, and W. Zhining, “A General Robot Inverse Kinematics Solution Method Based on Improved PSO Algorithm,” IEEE Access, vol. 9, pp. 32341–32350, 2021.
A. Jiping, L. Xinhong, Z. Zhang, W. Man, G. Zhang, and W. Ding, “Application of An Improved Particle Swarm Optimization Algorithm in Inverse Kinematics Solutions of Manipulators,” In IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 1680–1684, 2020.
K. Sanprasit, and P. Artrit, 2020. Multi-Objective Whale Optimization Algorithm for Balance Recovery of a Humanoid Robot. International Journal of Mechanical Engineering and Robotics Research, 9(6), pp.882-893, 2020.
X. Li, X. Zhang, H. Li, W. Yuan, and Y. Qiu, “Singularity processing algorithm for inverse kinematics of 6-DOF series robot,” In IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), vol. 2020, pp. 696–701, 2020.
M. Jin, Q. Liu, B. Wang, and H. Liu, “An Efficient and accurate Inverse Kinematics for 7-DOF Redundant Manipulators Based on a Hybrid of analytical and Numerical Method,” IEEE Access, vol. 8, pp. 16316–16330, 2020.
Y. Wang, X. Ding, Z. Tang, C. Hu, Q. Wei, and K. Xu, “Research Article A Novel Analytical Inverse Kinematics Method for SSRMS-Type Space Manipulators Based on the POE Formula and the Paden- Kahan Subproblem,” International Journal of Aerospace Engineering, 2021.
N. A. Mohamed, A. T. Azar, N. E. Abbas, M. A. Ezzeldin, and H. H. Ammar, “Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning,” in Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), pp. 283–295, 2020.
S. K. Shah, R. Mishra, and L. S. Ray, “Solution and validation of inverse kinematics using Deep artificial neural network,” Materials Today: Proceedings, vol. 26, pp. 1250–1254, 2020.
R. Bensadoun, S. Gur, N. Blau, T. Shenkar, and L. Wolf, “Neural Inverse Kinematics,” arXiv preprint, 2022.
J. Demby, Y. Gao, and G. N. Desouza, “A Study on Solving the Inverse Kinematics of Serial Robots using Artificial Neural Network and Fuzzy Neural Network,” IEEE International Conference on Fuzzy Systems, pp. 1–6, 2020.
X. Shi, Z. Guo, J. Huang, Y. Shen, and L. Xia, 2020, “A distributed reward algorithm for inverse kinematics of arm robot,” In 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), pp. 92-96, 2020.
A. Seyyedabbasi, R. Aliyev, F. Kiani, M. U. Gulle, H. Basyildiz, and M. A. Shah, “Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems,” Knowledge-Based Systems, vol. 223, p. 107044, 2021.
C. Choubey and J. Ohri, “Optimal Trajectory Generation for a 6-DOF Parallel Manipulator Using Grey Wolf Optimization Algorithm,” Robotica, vol. 39, no. 3, pp. 411-427, 2021.
G. Singh and V.K Banga, “Kinematics and Trajectory Planning Analysis Based on Hybrid Optimization Algorithms for an Industrial Robotic Manipulators,” 2022.
M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Systems with Applications, vol. 166, p. 113917, 2021.
S. Dereli, “A new modified grey wolf optimization algorithm proposal for a fundamental engineering problem in robotics,” Neural Computing and Applications, vol. 33, no. 21, pp. 14119-14131, 2021.
B. E. Nyong-Bassey, D. Giaouris, C. Patsios, S. Papadopoulou, Papadopoulos, A. I., S. Walker, and S. Gadoue, “Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty”, Energy, 193, 116622. 2020.
C. Lopez-Franco, D. Diaz, J. Hernandez-Barragan, N., Arana-Daniel, and M. Lopez-Franco, “A Metaheuristic Optimization Approach for Trajectory Tracking of Robot Manipulators”, Mathematics, 10(7), 1051, 2022.
J. S. Chou and D. N. Truong, D.N, “A novel metaheuristic optimizer inspired by behaviour of jellyfish in ocean”, Applied Mathematics and Computation, 389, p.125535, 2021.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer”, Advances in engineering software, vol. 69, pp. 46-61, 2014.
H. Kraiem, F. Aymen, L. Yahya, A. Triviño, M. Alharthi, and S. S. Ghoneim, “A comparison between particle swarm and grey wolf optimization algorithms for improving the battery autonomy in a photovoltaic system”, Applied Sciences, vol. 11, no. 16, pp. 7732, 2021.
Y. S. Kushwah, and R. Shrivastava, “Particle Swarm Optimization (PSO) Inspired Grey Wolf Optimization (GWO) Algorithm”. Int. J. Math. Trends Technol., vol. 58, pp. 81-91, 2018.
J. S. Wang, J. S. X. Li, “An improved grey wolf optimizer based on differential evolution and elimination mechanism. Scientific reports”, 9(1), 1-21, 2019.
B. E. Nyong-Bassey, and B. Akinloye, “Comparative study of optimized artificial intelligence based first order sliding mode controllers for position control of a DC motor actuator”. Journal of Automation, Mobile Robotics and Intelligent Systems, pp. 58-71, 2014.
Aljarah, I., Faris, H., & Mirjalili, S. (2018). Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing, 22(1), 1-15.
Ning, G. Y., & Cao, D. Q. (2021). Improved whale optimization algorithm for solving constrained optimization problems. Discrete Dynamics in Nature and Society, 2021.
M. Abdel-Basset, R. Mohamed, R.K. Chakrabortty, M.J. Ryan, and A. El-Fergany, “An improved artificial jellyfish search optimizer for parameter identification of photovoltaic models”, Energies,14(7), 2021, p.1867.
T. Zhang, W. Xin and W. Zhenlei, "A Novel Improved Grey Wolf Optimization Algorithm for Numerical Optimization and PID Controller Design", 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2018.