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A hybrid method using FABRIK and custom ANN in solving inverse kinematic for generic serial robot manipulator

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Abstract

Solving inverse kinematic (IK) of general robot manipulators remains significant challenge in current industrial manufacturing, particularly in human–robot collaborative scenarios. Most current approaches employ numerical, analytical, or machine learning methods to solve IK. However, accurately determining the end-effector (EE) position, solving complexity, and handling multiple solutions are unresolved challenges in these existing methods. In this paper, we propose a hybrid method that combines forward and backward reaching inverse kinematics (FABRIK) with a custom artificial neural network (ANN) to solve IK for a broad range of serial robot manipulators. The results demonstrate that the hybrid method yields a unique solution and achieves a lower position error (up to 0.003 in) compared to a standard ANN implementation. Furthermore, compared to the numerical method (FABRIK and Jacobian), the hybrid approach offers a more versatile framework for solving IK, resulting in superior overall performance in terms of solving complexity, computational efficiency, and accuracy among the three methods.

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Data availability

The code and data is available on GitHub https://github.com/baiye225/Inverse-Kinematics

References

  1. I Process Soluition. What are the Different types of industrial robots and their applications. https://processsolutions.com/what-are-the-different-types-of-industrial-robots-and-their-applications/#:~:text=Polar%20Robots%2C%20or%20spherical%20robots,have%20a%20spherical%20work%20envelope. Accessed 10/01/2018

  2. Groover MP (2023) Automation. Encyclopedia Britannica. https://www.britannica.com/technology/automation

  3. Mohammed AA, Sunar M (2015) Kinematics modeling of a 4-DOF robotic arm. In 2015 International Conference on Control, Automation and Robotics. IEEE, Singapore, pp 87–91. https://doi.org/10.1109/iccar.2015.7166008

  4. Qassem MA, Abuhadrous I, Elaydi H (2010) Modeling and simulation of 5 DOF educational robot arm. In: 2010 2nd International Conference on Advanced Computer Control, vol 5. IEEE, Shenyang, China, pp 569–574. https://doi.org/10.1109/icacc.2010.5487136

  5. Singh R, Kukshal V, Yadav VS (2021) A review on forward and inverse kinematics of classical serial manipulators. Adv Eng Design: Select Proceedings of ICOIED 2020:417–428

    Article  Google Scholar 

  6. Aristidou A, Lasenby J, Chrysanthou Y, Shamir A (2018) Inverse kinematics techniques in computer graphics: a survey. Comput Graph Forum 37(6):35–58

    Article  Google Scholar 

  7. Sekiguchi M, Takesue N (2021) Numerical method for inverse kinematics using an extended angle-axis vector to avoid deadlock caused by joint limits. Adv Robot 35(15):919–926

    Article  Google Scholar 

  8. Xie S, Sun L, Wang Z, Chen G (2022) A speedup method for solving the inverse kinematics problem of robotic manipulators. Int J Adv Robotic Syst 19(3):17298806221104602

    Google Scholar 

  9. Li J, Yu H, Shen N, Zhong Z, Lu Y, Fan J (2021) A novel inverse kinematics method for 6-DOF robots with non-spherical wrist. Mech Mach Theory 157:104180

    Article  Google Scholar 

  10. Lopez-Franco C, Hernandez-Barragan J, Alanis AY, Arana-Daniel N (2018) A soft computing approach for inverse kinematics of robot manipulators. Eng Appl Artif Intell 74:104–120

    Article  Google Scholar 

  11. Aristidou A, Lasenby J (2011) FABRIK: q fast, iterative solver for the Inverse Kinematics problem. Graph Models 73(5):243–260

    Article  Google Scholar 

  12. Semwal VB, Reddy M, Narad A (2021) Comparative study of inverse kinematics using data driven and Fabrik approach. In: Advances in Robotics-5th International Conference of The Robotics Society. Association for Computing Machinery, New York, NY, pp 1–6

  13. Rokbani N, Neji B, Slim M, Mirjalili S, Ghandour R (2022) A multi-objective modified PSO for inverse kinematics of a 5-DOF robotic arm. Appl Sci 12(14):7091

    Article  Google Scholar 

  14. Guan S, Zhuang Z, Tao H, Chen Y, Stojanovic V, Paszke W (2023) Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths. Trans Inst Meas Control 45(11):2015–2026

    Article  Google Scholar 

  15. Zhuang Z, Tao H, Chen Y, Stojanovic V, Paszke W (2022) An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems. IEEE. https://doi.org/10.1109/TSMC.2022.3225381

  16. Stojanović V (2023) Fault-tolerant control of a hydraulic servo actuator via adaptive dynamic programming. Math Model Cont 3(3):181–191. https://doi.org/10.3934/mmc.2023016

    Article  Google Scholar 

  17. Huda MAN, Susilo SH, Adhi PM (2022) Implementation of inverse kinematic and trajectory planning on 6-DOF robotic arm for straight-flat welding movement. Logic: Jurnal Rancang Bangun dan Teknologi 22(1):51–61

    Article  Google Scholar 

  18. Bai Y, Hsieh SJ (2023) Strategy with machine learning models for precise assembly using programming by demonstration. Int J Adv Manuf Technol 127:3699–3714

    Article  Google Scholar 

  19. Li G, Xiao F, Zhang X, Tao B, Jiang G (2022) An inverse kinematics method for robots after geometric parameters compensation. Mech Mach Theory 174:104903

    Article  Google Scholar 

  20. Dou R et al (2022) Inverse kinematics for a 7-DOF humanoid robotic arm with joint limit and end pose coupling. Mech Mach Theory 169:104637

    Article  Google Scholar 

  21. Xiao F et al (2021) An effective and unified method to derive the inverse kinematics formulas of general six-DOF manipulator with simple geometry. Mech Mach Theory 159:104265

    Article  Google Scholar 

  22. El-Sherbiny A, Elhosseini MA, Haikal AY (2018) A comparative study of soft computing methods to solve inverse kinematics problem. Ain Shams Eng J 9(4):2535–2548

    Article  Google Scholar 

  23. Varedi-Koulaei SM, Mokhtari M (2018) Trajectory tracking solution of a robotic arm based on optimized ANN. In: 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM). IEEE, Tehran, Iran, pp 76–81

  24. Aggarwal L, Aggarwal K, Urbanic RJ (2014) Use of artificial neural networks for the development of an inverse kinematic solution and visual identification of singularity zone (s). Procedia Cirp 17:812–817

    Article  Google Scholar 

  25. Duka A-V (2014) Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. Procedia Technol 12:20–27

    Article  Google Scholar 

  26. Daya B, Khawandi S, Akoum M (2010) Applying neural network architecture for inverse kinematics problem in robotics. J Softw Eng Appl 3(03):230

    Article  Google Scholar 

  27. Almusawi AR, Dülger LC, Kapucu S (2016) A new artificial neural network approach in solving inverse kinematics of robotic arm (Denso VP6242). Comput Intell Neurosci 2016:5720163. https://doi.org/10.1155/2016/5720163

  28. Iklima Z, Muthahhar MI, Khan A, Zody A (2021) Self-learning of delta robot using inverse kinematics and artificial neural networks. Sinergi 25(3):237. https://doi.org/10.22441/sinergi.2021.3.001

    Article  Google Scholar 

  29. Li J, Xu C, Chen Z, Bian S, Yang L, Lu C (2021) Hybrik: a hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. IEEE/CVF, pp 3383–3393

  30. Ananthanarayanan H, Ordóñez R (2015) Real-time inverse kinematics of (2n+ 1) DOF hyper-redundant manipulator arm via a combined numerical and analytical approach. Mech Mach Theory 91:209–226

    Article  Google Scholar 

  31. Ananthanarayanan H, Ordóñez R (2013) Real-time inverse kinematics of redundant manipulator using a hybrid (analytical and numerical) method. In: 2013 16th International Conference on Advanced Robotics (ICAR). IEEE, Montevideo, Uruguay, pp 1–6

  32. Hasan AT, Ismail N, Hamouda AMS, Aris I, Marhaban MH, Al-Assadi H (2010) Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations. Adv Eng Softw 41(2):359–367

    Article  Google Scholar 

  33. Grochow K, Martin SL, Hertzmann A, Popović Z (2004) Style-based inverse kinematics, in ACM SIGGRAPH. Papers 2004:522–531

    Google Scholar 

  34. McCrate MP (2010) Modern mechanical automata. University of Cincinnati

    Google Scholar 

  35. FlexiBowl. SCARA Robot. https://www.flexibowl.com/scara-robot.html. Accessed 05/11/2020

  36. Mohammed AA, Sunar M (2015) Kinematics modeling of a 4-DOF robotic arm. In: 2015 International Conference on Control, Automation and Robotics. IEEE, Singapore, pp 87–91

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Authors and Affiliations

  1. Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA

    Ye Bai

  2. Department of Engineering Technology (Joint Appointment With Department of Mechanical Engineering), Texas A&M University, College Station, TX, USA

    Sheng-Jen Hsieh

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  1. Ye Bai
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All authors contributed conception and data analysis. The methodology, program, experiment design and setup, and first draft manuscript written by Ye Bai and all authors made comments and revisions.

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Correspondence to Ye Bai.

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Bai, Y., Hsieh, SJ. A hybrid method using FABRIK and custom ANN in solving inverse kinematic for generic serial robot manipulator. Int J Adv Manuf Technol 130, 4883–4904 (2024). https://doi.org/10.1007/s00170-023-12928-3

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