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Improved Algorithm for Solving Inverse Kinematics of Biped Robots

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Abstract

Inverse kinematics is an important basic theory in walking control of biped robot. This study focuses on the parameter setting using the improved algorithm in inverse kinematics. By analyzing the process of whether can the robot legs arrive at the expected positions from different initial positions, the parameter value range is determined. It must be noted that, the parameter values exhibit clear physical significance. The robot legs can move stably within the allowable value range. Furthermore, the superiority of the improved algorithm was validated by 3D simulation of leg motion. Moreover, the present study can provide theoretical basis for optimizing the leg motion of biped robot and developing the related prototype.

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References

  1. Kajita, S.; Benallegue, M.; Cisneros, R.; et al. Position-based lateral balance control for knee-stretched biped robot. 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, Canada, 2019, 17–24.

  2. Huan, T.T.; Van Kien, C.; Anh, H.P.H. Optimized gait planning of biped robot using multi-objective JAYA algorithm. International Journal of Advanced Robotic Systems, 2020, 1–13.

  3. Fadli, H.; Machbub, C.; Hidayat, E. Human gesture imitation on NAO humanoid robot using kinect based on inverse kinematics method. 2017 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), Surabaya, Indonesia, 2017, 116–120.

  4. Zamparelli A, Scianca N, Lanari L et al (2018) Humanoid gait generation on uneven ground using intrinsically stable MPC. IFAC-Papers OnLine 51(22):393–398

    Article  Google Scholar 

  5. Winkler AW, Farshidian F, Pardo D et al (2017) Fast trajectory optimization for legged robots using vertex-based ZMP constraints. IEEE Robotics and Automation Letters 2(4):2201–2208

    Article  Google Scholar 

  6. Kajita, S.; Benallegue, M.; Cisneros, R.; et al. Biped gait control based on spatially quantized dynamics. 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China, 2018, 75–81.

  7. Xie T, Xu JF, Zhang YX et al (2002) History, current state, and prospect of study of humanoids. Robot 24(4):367–374

    Google Scholar 

  8. Phaniteja, S.; Dewangan, P.; Guhan, P.; et al. A deep reinforcement learning approach for dynamically stable inverse kinematics of humanoid robots. 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau SAR, China, 2017, 1818–1823.

  9. Buss SR, Kim JS (2005) Selectively damped least squares for inverse kinematics. Journal of Graphics, GPU & Game Tools 10(3):37–49

    Article  Google Scholar 

  10. Faraji S, Ijspeert AJ (2017) Singularity-tolerant inverse kinematics for bipedal robots: an efficient use of computational power to reduce energy consumption. IEEE Robotics and Automation Letters 2(2):1132–1139

    Article  Google Scholar 

  11. Schumacher C, Knoop E, Bacher M (2021) A versatile inverse kinematics formulation for retargeting motions onto robots with kinematic loops. IEEE Robotics and Automation Letters 6(2):943–950

    Article  Google Scholar 

  12. Phuoc LM, Martinet P, Lee S et al (2008) Damped least square based genetic algorithm with Ggaussian distribution of damping factor for singularity robust inverse kinematics. J Mech Sci Technol 22(4):1330–1338

    Article  Google Scholar 

  13. Sugihara, T.; Yamamoto, T. Foot-guided agile control of a biped robot through ZMP manipulation. In: IEEE International Conference on Intelligent Robots and Systems, Vancouver, Canada, 2017, 4546–4551.

  14. Czarnetzki S, Kerner S, Urbann O (2009) Observer-based dynamic walking control for biped robots. Robot Auton Syst 57:839–845

    Article  Google Scholar 

  15. Caron, S.; Kheddar, A.; Tempier, O. Stair climbing stabilization of the HRP-4 humanoid robot using whole-body admittance control. IEEE International Conference on Robotics and Automation, Montreal, Canada, 2019, 277–283.

  16. Xi A, Mudiyanselage W, Tao DC et al (2019) Balance control of a biped robot on a rotating platform based on efficient reinforcement learning. Ieee/Caa Journal of Automatica Sinica 6(4):938–951

    Article  MathSciNet  Google Scholar 

  17. Neo ES, Yokoi K, Kajita S et al (2005) A switching command-based whole-body operation method for humanoid robots. IEEE/ASME Trans Mechatron 10(5):546–559

    Article  Google Scholar 

  18. Sellaouti, R.; Stasse, O.; Kajita, S.; et al. Faster and smoother walking of humanoid HRP-2 with passive toe joints. IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ, USA: IEEE, 2006, 4909–4914.

  19. Aoi, S.; Egi, Y.; Ichikawa, A.; et al. Experimental verification of gait transition from quadrupedal to bipedal locomotion of an oscillator-driven biped robot. IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ, USA: IEEE, 2008, 1115–1120.

  20. Winkler AW, Bellicoso CD, Hutter M et al (2018) Gait and trajectory optimization for legged systems through phase-based end-effector parameterization. IEEE Robotics and Automation Letters 3(3):1560–1567

    Article  Google Scholar 

  21. Al-Shuka HFN (2018) On local approximation-based adaptive control with applications to robotic manipulators and biped robot. International Journal of Dynamics and Control 6:339–353

    Article  MathSciNet  Google Scholar 

  22. Geilinger M, Poranne R, Desai R et al (2018) Skaterbots: Optimization-based design and motion synthesis for robotic creatures with legs and wheels. ACM Transactions on Graphics 37(4):1–12

    Article  Google Scholar 

  23. Khusainov R, Afanasyev I, Sabirova L et al (2016) Bipedal robot locomotion modelling with virtual height inverted pendulum and preview control approaches in Simulink environment. Journal of Robotics, Networking and Artificial Life 3(3):182–187

    Article  Google Scholar 

  24. Shimmyo S, Sato T, Ohnishi K (2013) Biped walking pattern generation by using preview control based on three-mass model. IEEE Trans Industr Electron 60(11):5137–5147

    Article  Google Scholar 

  25. Reinhart, R.F.; Steil, J.J. Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA10 robot. ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems. Edinburgh: IEEE, 2008, 35–40.

  26. Kajita S (2007) Guan trans. Y.S. Humanoid robots. Tsinghua University Press, Beijing, China

    Google Scholar 

  27. Jing, C.L.; Li, Z.S.; Xue, F.Z. Fuzzy adaptive algorithm for biped robot inverse kinematics. Robot, 2010, 32(4), 534–539+546.

  28. Jing CL, Li ZS, Xue FZ (2010) Tri-dimensional simulation of biped robot about kinematics. Computer Simulation 27(7):153–156

    Google Scholar 

  29. Jing CL, Zhu XM (2014) Tri - dimensional simulation of walking control algorithm for biped robot. Computer Simulation 31(3):346–350

    MathSciNet  Google Scholar 

  30. Qian ZZ, Zhou HP, Zhang P (1995) Application of fuzzy logic in solution of inverse kinematics. Fuzzy Systems and Mathematics 9(4):26–31

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This research is financially supported by Science and Technology Plan of Guizhou Province of China ([2018]1024 and [2017]1017).

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Correspondence to Chenglin Jing.

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Jing, C., Zheng, J. Improved Algorithm for Solving Inverse Kinematics of Biped Robots. Mobile Netw Appl 27, 975–983 (2022). https://doi.org/10.1007/s11036-022-01912-y

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