Inverse Kinematics Analysis of Novel 6-DOF Robotic Arm Manipulator for Oil and Gas Welding Using Meta-Heuristic Algorithms
Main Article Content
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)
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
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: J. Rancang Bangun Teknol., vol. 22, no. 1, pp. 51–61, 2022.
DOI: https://doi.org/10.31940/logic.v22i1.51-61.
M. Saraf, A. Agarwal, A. Chaudhary, and A. Ganthale, “Kinematic modelling and motion mapping of robotic arms,” J. Phys.: Conf. Ser., vol. 1969, no. 1, 2021.
DOI: https://doi.org/10.1088/1742-6596/1969/1/012002.
M. T. Nguyen, C. Yuan, and J. H. Huang, “Kinematic analysis of a 6-DOF robotic arm,” IFToMM World Congr. Mech. Mach. Sci., vol. 73, no. 100, pp. 2965–2974, 2019.
URL:https://link.springer.com/chapter/10.1007/978-3-030-20131-9_292. (Accessed: 3 Apr 2022).
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.
DOI: https://doi.org/10.21496/ams.2020.018.
S. Ondocko et al., “Inverse kinematics data adaptation to non-standard modular robotic arm consisting of unique rotational modules,” Appl. Sci., vol. 11, no. 3, pp. 1–15, 2021.
DOI: https://doi.org/10.3390/app11031203.
A. R. Al Tahtawi, M. Agni, and T. D. Hendrawati, “Small-scale robot arm design with pick and place mission based on inverse kinematics,” J. Robot. Control (JRC), vol. 2, no. 6, pp. 469–475, 2021.
DOI: https://doi.org/10.18196/26124.
A. Saadah, “Computing the kinematics study of a 6-DOF industrial manipulator prototype by Matlab,” Recent Innov. Mechatron., vol. 7, no. 1, 2021.
DOI: https://doi.org/10.17667/riim.2020.1/8.
S. Dereli and R. Köker, “A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum-behaved particle swarm algorithm,” Artif. Intell. Rev., vol. 53, no. 2, pp. 949–964, 2020.
DOI: https://doi.org/10.1007/s10462-019-09683-x.
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.
DOI: https://doi.org/10.1016/j.ifacol.2020.12.2695.
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,” Front. Neurorobot., vol. 16, pp. 1–12, 2022.
DOI: https://doi.org/10.3389/fnbot.2022.791796.
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.
DOI: https://doi.org/10.1109/ACCESS.2021.3059714.
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 Proc. IEEE 9th Joint Int. Inf. Technol. Artif. Intell. Conf. (ITAIC), 2020, pp. 1680–1684.
DOI: https://doi.org/10.1109/ITAIC49862.2020.9339042.
K. Sanprasit and P. Artrit, “Multi-objective whale optimization algorithm for balance recovery of a humanoid robot,” Int. J. Mech. Eng. Robot. Res., vol. 9, no. 6, pp. 882–893, 2020.
DOI: https://doi.org/10.18178/ijmerr.9.6.882-893.
X. Li, X. Zhang, H. Li, W. Yuan, and Y. Qiu, “Singularity processing algorithm for inverse kinematics of 6-DOF series robot,” in Proc. IEEE 9th Joint Int. Inf. Technol. Artif. Intell. Conf. (ITAIC), 2020, pp. 696–701.
DOI: https://doi.org/10.1109/ITAIC49862.2020.9338762.
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.
DOI: https://doi.org/10.1109/ACCESS.2020.2966768.
Y. Wang, X. Ding, Z. Tang, C. Hu, Q. Wei, and K. Xu, “A novel analytical inverse kinematics method for SSRMS-type space manipulators based on the POE formula and the Paden-Kahan subproblem,” Int. J. Aerosp. Eng., 2021.
DOI: https://doi.org/10.1155/2021/6690696.
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 Proc. Int. Conf. Artif. Intell. Comput. Vis. (AICV2020), 2020, pp. 283–295.
DOI: https://doi.org/10.1007/978-3-030-44289-7_27.
S. K. Shah, R. Mishra, and L. S. Ray, “Solution and validation of inverse kinematics using deep artificial neural network,” Mater. Today: Proc., vol. 26, pp. 1250–1254, 2020.
DOI: https://doi.org/10.1016/j.matpr.2020.02.250.
R. Bensadoun, S. Gur, N. Blau, T. Shenkar, and L. Wolf, “Neural inverse kinematics,” arXiv preprint, 2022.
DOI: https://doi.org/10.48550/arXiv.2205.10837.
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,” in Proc. IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE), 2019, pp. 1–6.
DOI: https://doi.org/10.1109/FUZZ-IEEE.2019.8858872.
X. Shi, Z. Guo, J. Huang, Y. Shen, and L. Xia, “A distributed reward algorithm for inverse kinematics of arm robot,” in Proc. 2020 5th Int. Conf. Autom. Control Robot. Eng. (CACRE), pp. 92–96, 2020. DOI: https://doi.org/10.1109/CACRE50138.2020.9230347.
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 Syst., vol. 223, p. 107044, 2021. DOI: https://doi.org/10.1016/j.knosys.2021.107044.
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.
DOI: https://doi.org/10.1017/s0263574720000442.
G. Singh and V. K. Banga, “Kinematics and trajectory planning analysis based on hybrid optimization algorithms for industrial robotic manipulators,” 2022.
DOI: https://doi.org/10.21203/rs.3.rs-1313895/v1.
M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Syst. Appl., vol. 166, p. 113917, 2021.
DOI: https://doi.org/10.1016/j.eswa.2020.113917.
S. Dereli, “A new modified grey wolf optimization algorithm proposal for a fundamental engineering problem in robotics,” Neural Comput. Appl., vol. 33, no. 21, pp. 14119–14131, 2021. DOI: https://doi.org/10.1007/s00521-021-06050-2
B. E. Nyong-Bassey, D. Giaouris, C. Patsios, S. Papadopoulou, A. I. Papadopoulos, 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, vol. 193, p. 116622, 2020.
DOI: https://doi.org/10.1016/j.energy.2019.116622.
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, vol. 10, no. 7, p. 1051, 2022.
DOI: https://doi.org/10.3390/math10071051.
J. S. Chou and D. N. Truong, "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Appl. Math. Comput., vol. 389, p. 125535, 2021.
DOI: https://doi.org/10.1016/j.amc.2020.125535.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014.
DOI: https://doi.org/10.1016/j.advengsoft.2013.12.007
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,” Appl. Sci., vol. 11, no. 16, p. 7732, 2021. DOI: https://doi.org/10.3390/app11167732
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.
DOI: https://doi.org/10.14445/22315373/IJMTT-V58P520.
J. S. Wang and J. S. X. Li, “An improved grey wolf optimizer based on differential evolution and elimination mechanism. Scientific Reports,” 9(1), pp. 1–21, 2019.
DOI: https://doi.org/10.1038/s41598-019-43546-3.
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,” J. Autom. Mob. Robot. Intell. Syst., pp. 58–71, 2014.
DOI: https://doi.org/10.14313/JAMRIS_3-2016/25.
I. Aljarah, H. Faris, and S. Mirjalili, “Optimizing connection weights in neural networks using the whale optimization algorithm,” Soft Comput., vol. 22, no. 1, pp. 1–15, 2018.
DOI: https://doi.org/10.1007/s00500-016-2442-1.
G. Y. Ning and D. Q. Cao, “Improved whale optimization algorithm for solving constrained optimization problems,” Discrete Dyn. Nat. Soc., 2021.
DOI: https://doi.org/10.1155/2021/8832251.
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, vol. 14, no. 7, p. 1867, 2021.
DOI: https://doi.org/10.3390/en14071867.
T. Zhang, W. Xin, and W. Zhenlei, "A novel improved grey wolf optimization algorithm for numerical optimization and PID controller design," in Proc. 2018 IEEE 7th Data Driven Control and Learning Syst. Conf. (DDCLS), 2018.