(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-97 December-2022
Full-Text PDF
Paper Title : Inverse kinematics solutions of a newly designed three-link robotic manipulator for the casting process using the ant lion optimizer
Author Name : Mahendra Kumar Jangid, Sunil Kumar and Jagtar Singh
Abstract :

Conventional methods make determining inverse kinematics solutions of a multi-degree of freedom robot difficult. In recent years, soft computing methods have been used, and they are very easy to compute the solutions of inverse kinematics. In this study, the ant lion optimizer (ALO) was used to solve the inverse kinematics of a three-link robotic manipulator, and the results have been compared to other optimization algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), and the sine-cosine algorithm (SCA). In the beginning, Denavit-Hartenberg (D-H) parameters for the robotic manipulator are constructed, as well as transformation matrices. The entire transformation matrix is then used to find the end-effector position equations. The ALO, PSO, GWO, and SCA algorithms are used to predict the end-effector location of this robotic manipulator in the workspace. The location error (difference between the real and goal positions) is estimated using a fitness function. The fitness function was used to find the inverse kinematic solutions by reducing the position error. These algorithms were put to the test in this study using two separate scenarios. Position error and solution time for a single point in the workspace were calculated in case I, while position error and solution time for twenty randomly selected locations in the workspace were estimated in case II. After computation, the ALO, PSO, GWO, and SCA give position errors of 6.557 e-06, 0.00835, 0.006881, and 0.00993 meters respectively, for case I. The solution times for the ALO, PSO, GWO, and SCA are 0.88, 14.34, 2.01, and 1.31 seconds, respectively, for case I. Similarly, better results were found for case II as compared to case I in terms of position error and solution time. By comparing case-II to case-I, case-II confirms the quality of the ALO when compared to other optimization algorithms (PSO, GWO, and SCA). In terms of position error and solution time, the ALO algorithm performs significantly better than the PSO, GWO, and SCA algorithms.

Keywords : ALO, Inverse kinematics, Three-link robotic manipulator, PSO, GWO, SCA, Optimization algorithms.
Cite this article : Jangid MK, Kumar S, Singh J. Inverse kinematics solutions of a newly designed three-link robotic manipulator for the casting process using the ant lion optimizer. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(97):1704-1717. DOI:10.19101/IJATEE.2021.876125.
References :
[1]Yahya S, Moghavvemi M, Yang SS, Mohamed HA. Motion planning of hyper redundant manipulators based on a new geometrical method. In international conference on industrial technology. 2009 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[2]Sheng L, Yiqing W, Qingwei C, Weili H. A new geometrical method for the inverse kinematics of the hyper-redundant manipulators. In international conference on robotics and biomimetics 2006 (pp. 1356-9). IEEE.
[Crossref] [Google Scholar]
[3]Olsen AL, Petersen HG. Inverse kinematics by numerical and analytical cyclic coordinate descent. Robotica. 2011; 29(4):619-26.
[Crossref] [Google Scholar]
[4]Perez A, Mccarthy JM. Sizing a serial chain to fit a task trajectory using clifford algebra exponentials. In proceedings of the international conference on robotics and automation 2005 (pp. 4709-15). IEEE.
[Crossref] [Google Scholar]
[5]http://ethesis.nitrkl.ac.in/6980/1/2015_Panchanand_Phd_511ID101.pdf. Accessed 10 April 2022.
[6]Rokbani N, Alimi AM. Inverse kinematics using particle swarm optimization, a statistical analysis. Procedia Engineering. 2013; 64:1602-11.
[Crossref] [Google Scholar]
[7]Huang HC, Chen CP, Wang PR. Particle swarm optimization for solving the inverse kinematics of 7-DOF robotic manipulators. In international conference on systems, man, and cybernetics 2012 (pp. 3105-10). IEEE.
[Crossref] [Google Scholar]
[8]Deng H, Xie C. An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators. Soft Computing. 2021; 25(21):13695-708.
[Crossref] [Google Scholar]
[9]Sancaktar I, Tuna B, Ulutas M. Inverse kinematics application on medical robot using adapted PSO method. Engineering Science and Technology, an International Journal. 2018; 21(5):1006-10.
[Crossref] [Google Scholar]
[10]Dereli S, Köker R. IW-PSO approach to the inverse kinematics problem solution of a 7-DOF serial robot manipulator. Sigma Journal of Engineering and Natural Sciences. 2018; 36(1):77-85.
[Google Scholar]
[11]Alkayyali M, Tutunji TA. PSO-based algorithm for inverse kinematics solution of robotic arm manipulators. In international conference on research and education in mechatronics 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[12]Dereli S, Köker R. Calculation of the inverse kinematics solution of the 7-DOF redundant robot manipulator by the firefly algorithm and statistical analysis of the results in terms of speed and accuracy. Inverse Problems in Science and Engineering. 2020; 28(5):601-13.
[Crossref] [Google Scholar]
[13]Dereli S, Köker R. Simulation based calculation of the inverse kinematics solution of 7-DOF robot manipulator using artificial bee colony algorithm. SN Applied Sciences. 2020; 2(1):1-11.
[Crossref] [Google Scholar]
[14]El-sherbiny A, Elhosseini MA, Haikal AY. A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm. Applied Soft Computing. 2018; 73:24-38.
[Crossref] [Google Scholar]
[15]Dereli S, Köker R. A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm. Artificial Intelligence Review. 2020; 53(2):949-64.
[Crossref] [Google Scholar]
[16]Ayyıldız M, Çetinkaya K. Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator. Neural Computing and Applications. 2016; 27(4):825-36.
[Crossref] [Google Scholar]
[17]Köker R, Çakar T. A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study. Engineering with Computers. 2016; 32(4):553-65.
[Crossref] [Google Scholar]
[18]Köker R. A neuro-simulated annealing approach to the inverse kinematics solution of redundant robotic manipulators. Engineering with Computers. 2013; 29(4):507-15.
[Crossref] [Google Scholar]
[19]Duka AV. Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. Procedia Technology. 2014; 12:20-7.
[Crossref] [Google Scholar]
[20]Park JK. Inverse kinematics based on fuzzy logic and neural networks for the WAM-titan II teleoperation system. Masters Thesis, University of Tennessee, 2007.
[Google Scholar]
[21]Gao R. Inverse kinematics solution of robotics based on neural network algorithms. Journal of Ambient Intelligence and Humanized Computing. 2020; 11(12):6199-209.
[Crossref] [Google Scholar]
[22]Gong M, Li X, Zhang L. Analytical inverse kinematics and self-motion application for 7-DOF redundant manipulator. IEEE Access. 2019; 7:18662-74.
[Crossref] [Google Scholar]
[23]Tevatia G, Schaal S. Inverse kinematics for humanoid robots. In proceedings of international conference on robotics and automation. symposia proceedings (Cat. No. 00CH37065) 2000 (pp. 294-9). IEEE.
[Crossref] [Google Scholar]
[24]Rokbani N, Mirjalili S, Slim M, Alimi AM. A beta salp swarm algorithm meta-heuristic for inverse kinematics and optimization. Applied Intelligence. 2022:1-26.
[Crossref] [Google Scholar]
[25]Ghafil HN, Jármai K. Optimization algorithms for inverse kinematics of robots with MATLAB source code. In vehicle and automotive engineering 2020 (pp. 468-77). Springer, Singapore.
[Crossref] [Google Scholar]
[26]Xu J, Wang W, Sun Y. Two optimization algorithms for solving robotics inverse kinematics with redundancy. Journal of Control Theory and Applications. 2010; 8(2):166-75.
[Crossref] [Google Scholar]
[27]El-sherbiny A, Elhosseini MA, Haikal AY. A comparative study of soft computing methods to solve inverse kinematics problem. Ain Shams Engineering Journal. 2018; 9(4):2535-48.
[Crossref] [Google Scholar]
[28]Soylak M, Oktay T, Turkmen İ. A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 2017; 231(3):470-9.
[Crossref] [Google Scholar]
[29]Yiyang L, Xi J, Hongfei B, Zhining W, Liangliang S. A general robot inverse kinematics solution method based on improved PSO algorithm. IEEE Access. 2021; 9:32341-50.
[Crossref] [Google Scholar]
[30]Rahkar FT. Battle royale optimization algorithm. Neural Computing and Applications. 2021; 33(4):1139-57.
[Crossref] [Google Scholar]
[31]Šegota SB, Anđelić N, Mrzljak V, Lorencin I, Kuric I, Car Z. Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator. International Journal of Advanced Robotic Systems. 2021; 18(4):1-11.
[Crossref] [Google Scholar]
[32]Dereli S. A new modified grey wolf optimization algorithm proposal for a fundamental engineering problem in robotics. Neural Computing and Applications. 2021; 33(21):14119-31.
[Crossref] [Google Scholar]
[33]Mani M, Bozorg-haddad O, Chu X. Ant lion optimizer (ALO) algorithm. In advanced optimization by nature-inspired algorithms 2018 (pp. 105-16). Springer, Singapore.
[Crossref] [Google Scholar]
[34]Mirjalili S. The ant lion optimizer. Advances in Engineering Software. 2015; 83:80-98.
[Crossref] [Google Scholar]
[35]Jangid MK, Kumar S, Singh J. Trajectory tracking optimization and control of a three link robotic manipulator for application in casting. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(83):1255-67.
[Crossref] [Google Scholar]
[36]Shi Q, Xie J. A research on inverse kinematics solution of 6-DOF robot with offset-wrist based on adaboost neural network. In international conference on cybernetics and intelligent systems (CIS) and conference on robotics, automation and mechatronics 2017 (pp. 370-5). IEEE.
[Crossref] [Google Scholar]