Abstract
Inverse kinematics is one of the most important, most researched but still one among the most challenging problems in the robotics domain, for problems like motion generation and trajectory optimization. Various attempts have been made to propose a neural network that can solve the inverse kinematics problems. But not much emphasis has been given to analyze and compare its performance with other state-of-the-art methods. The major contribution of this paper is to present the performance analysis of one of the best neural networks proposed so far, and compare its results with the analytical approach. The main reason for using data-driven techniques like neural networks for inverse kinematics solution of robotic manipulators is that it can be extended to any number of links without much effort, while in other methods we have to consider number of links, types of links beforehand...
The work is funded by SERB, DST, Govt. of India to Dr. Vijay Bhaskar Semwal under the schema of Early career award, DST No: ECR/2018/000203 dated on 04/06/2019.
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References
Semwal, V.B., Gaud, N., Nandi, G.C.: Human gait state prediction using cellular automata and classification using ELM. In: Tanveer, M., Pachori, R. (eds.) Machine Intelligence and Signal Analysis. Springer, Singapore, pp. 135–145 (2019). https://doi.org/10.1007/978-981-13-0923-6_12
Gao, H., He, W., Zhou, C., Sun, C.: Neural network control of a two-link flexible robotic manipulator using assumed mode method. IEEE Trans. Ind. Inform. https://doi.org/10.1109/TII.2018.2818120
Almusawi, A. R. J., Dülger, L. C., and Kapucu, S.: A new artificial neural network approach in solving inverse kinematics of robotic arm (Denso VP6242). In: Computational Intelligence and Neuroscience 2016 (2016). https://doi.org/10.1155/2016/5720163
Semwal, V.B., Kumar, C., Mishra, P.K., Nandi, G.C.: Design of vector field for different subphases of gait and regeneration of gait pattern. IEEE Trans. Autom. Sci. Eng. 15(1), 104–110 (2016). https://doi.org/10.1109/TASE.2016.2594191
Semwal, V.B., Katiyar, S.A., Chakraborty, R., Nandi, G.C.: Biologically-inspired push recovery capable bipedal locomotion modeling through hybrid automata. Robot. Auton. Syst. 70, 181–190 (2015) https://doi.org/10.1016/j.robot.2015.02.009
Semwal, V.B., Nandi, G.C.: Toward developing a computational model for bipedal push recovery-a brief. IEEE Sens. J. 15(4), 2021–2022 (2015). https://doi.org/10.1109/JSEN.2015.2389525
Nandi, G. C., Semwal, V.B., Raj, M., Jindal, A.: Modeling bipedal locomotion trajectories using hybrid automata. In: 2016 IEEE Region 10 Conference (TENCON). IEEE (2016). https://doi.org/10.1109/TENCON.2016.7848159
Semwal, V.B., Nandi, G.C.: Generation of joint trajectories using hybrid automate-based model: a rocking block-based approach. IEEE Sens. J. 16(14), 5805–5816 (2016). https://doi.org/10.1109/JSEN.2016.2570281
Kucuk, S., Bingul, Z.: Robot kinematics: forward and inverse kinematics. In: Cubero, S. (ed.) Industrial Robotics: Theory, Modelling and Control. InTech (2006). https://doi.org/10.5772/5015
Hasan, A.T., Al-Assadi, H.M.A.A.A., Isa, A.A.M.: Neural network’s based inverse kinematics solution for serial robot manipulators passing through singularities. In: Suzuki, K. (ed.) Artificial Neural Networks-Industrial and Control Engineering Applications. InTech (2011). https://doi.org/10.5772/14977
Takehiko, O., Hajime, K.: Solution of ill-posed inverse kinematics of robot arm by network inversion. J. Robot. (2010). https://doi.org/10.1155/2010/870923
Duka, A.V.: Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. In: Elsevier Conference on Interdisciplinarity in Engineering (INTER-ENG 2013). https://doi.org/10.1016/j.protcy.2013.12.451
Stevo, S., Sekaj, I., Dekan, M.: Optimization of robotic arm trajectory using genetic algorithm. In:19th World Congress the International Federation of Automatic Control (IFAC) (2014). https://doi.org/10.13140/2.1.4466.1123
Rokbani, N., Alimi, A.M.: Inverse kinematics using particle swarm optimization, a statistical analysis. Elsevier (2013). https://doi.org/10.1016/j.proeng.2013.09.242
Aristidou, A., Lasenby, J.: Inverse Kinematics: a review of existing techniques and introduction of a new fast iterative solver. Technical Report, University of Cambridge, CUED/F-INFENG/TR-632 (2009)
Groetsch, C.W.: Inverse Problems in the Mathematical Sciences. Springer (1993). https://doi.org/10.1007/978-3-322-99202-4
Tejomurtula, S., Kak, S.: Inverse Kinematics in robotics using neural networks. Inf. Sci. 116, 147–164 (1999). https://doi.org/10.1016/S0020-0255(98)10098-1
Bingul, Z., Ertunc, H.M., Oysu, C.: Applying neural network to inverse kinematics problem of 6R robot manipulator with offset wrist. In: Ribeiro B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds.) Adaptive and Natural Computing Algorithms, pp. 112–115 (2005). https://doi.org/10.1007/3-211-27389-1_27
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This work is funded by SERB, DST, govt. of India for project under the schema of Early career award (ECR) with file no: ECR/2018/000203 dated on 04/06/2019. to Dr. Vijay Bhaskar Semwal.
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Semwal, V.B., Gupta, Y. (2022). Performance Analysis of Data-Driven Techniques for Solving Inverse Kinematics Problems. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_6
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