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Humanoid robot runs up-down stairs using zero-moment with supporting polygons control

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Abstract

The humanoid robots have similar human feet, but due to the limitations of joint freedom, they are difficult to walk on the special ground like humans, so planning an effective walking method applies on these special grounds becomes of critical importance. As a typical example of a special ground, this paper proposes a dynamic planning method for the gait planning problem of the humanoid robot NAO climbing and down stairs. Firstly, the NAO robot is modeled by the kinematics; then it can be understood the humanoid robot itself after the gait characteristics and human walking patterns according to the characteristics of the stairs, and the geometric constraints of the robot’s walking gaits on the stairs are analyzed; further then the robot’s lateral gait is planned through a first-order linear inverted pendulum model, and the variable length model is used. The inverted pendulum model is used to plan the forward gait of the robot, and then the start and stop gaits of the robot are planned respectively and meanwhile we proposed the zero-moment point stability judgment and the supporting polygons control to determine whether the robot is in a stable state. In the three-dimensional stairs, the visual system of the NAO robot was used to perceive its surrounding environment, and then the image processing technology was used to identify the position of the stairs. Finally, the correctness of the gait planning is verified by the Webots Platform for NAO simulation and its real operation. The experimental results show that the gait planning method used in this paper is highly feasible.

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References

  1. Aldebaran Robotics (n.d.) NAO Software documentation [EB/OL]. http://doc.aldebaran.com/2-1/index.html. Accessed 1 Apr 2022

  2. Alexiadis DS, Zarpalas D (2013) Real-time, full 3-D reconstruction of moving foreground objects from multiple consumer depth cameras. IEEE Trans Multimedia 15(2):339–358

    Article  Google Scholar 

  3. Alsharif MH, Kelechi AH, Yahya K, Chaudhry SA (2020) Machine learning algorithms for smart data analysis in internet of things environment: taxonomies and research trends. Symmetry 12(1):88

    Article  Google Scholar 

  4. Armesto L, Tornero J (2009) Automation of industrial vehicles: A vision based line tracking application. In: Proc. IEEE Conference on Emerging Technologies & Factory Automation, pp. 1–7

  5. Bharati P, Pramanik A (2019) Deep learning techniques—R-CNN to mask R-CNN: a survey puja bharati & ankita pramanik conference paper first online: 18 August 2019 computational intelligence in pattern recognition pp 657–668

  6. Bi ZM, Zhang WJ, Chen IM, Lang SYT (2007) Automated geneartion of the D-H parameters for configuration design of modular manipulators. Robot Comput Integr Manuf 23(5):553–562

    Article  Google Scholar 

  7. Chen G, Wang J, Wang L (2014) Gait planning and compliance control of a biped robot on stairs with desired ZMP. IFAC Proc Vol 47(3):2165–2170

    Article  Google Scholar 

  8. Deng C, Li Z (2018) Human-Guided Robotic Exoskeleton Cooperative Walking for Climbing Stairs, 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), IEEE, 18–20

  9. Hirai K, Hirose M, Haikawa Y, et al. (1998) The development of Honda humanoid robot. Hirai K, Hirose M, & T. Takenaka, Published 16 May 1998 Computer Science Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), pp. 1321–1326

  10. Kalita DJ, Prakash V, Kumar SV (2020) A survey on SVM hyper-parameters optimization techniques, Social Networking and Computational Intelligence. Springer, Singapore, pp 243–256

  11. Li J (2018) Research on mobile robot target recognition and obstacle avoidance based on vision, J Int Technol, 19(6):1–14

  12. Lin H, Wang Y, Zhang Y (2012) Dynamics analysis based on ADAMS manipulator. Manuf Autom 34(22):80–83

    Google Scholar 

  13. Luo Y, Qin J, Xiang X, Tan Y, Liu Q, Xiang L (2020) Coverless real-time image information hiding based on image block matching and dense convolutional network. J Real-Time Image Proc 17(1):125–135

    Article  Google Scholar 

  14. Martinez S, Cortes J, Bullo F (2007) Motion coordination with distributed information. IEEE Control Syst 27(4):75–88

    Article  Google Scholar 

  15. Morrison JG, Gavez-Lopez D, Sibley G (2016) Scalable multirobot localization and mapping with relative maps: introducing MOARSLAM. IEEE Control Syst 36(2):75–85

    Article  MathSciNet  MATH  Google Scholar 

  16. Nasrinahar A, Chuah JH (2018) Intelligent motion planning of a mobile robot with dynamic obstacle. J Veh Routing Algorithm 1:89–104

    Article  Google Scholar 

  17. Olszewska JI, Toman J (2016.) OPEN: New Path-Planning Algorithm for Real-World Complex Environment, International Conference on Innovative Techniques and Applications of Artificial Intelligence SGAI 2016: Research and Development in Intelligent Systems XXXIII Conference paper First Online: 05 November 2016, pp. 237–244

  18. Olszewska JI, Houghtaling M et al (2020) Robotic standard development life cycle in action. J Intell Robot Syst 98:119–131

    Article  Google Scholar 

  19. Oriolo G, Ulivi G, Vendittelli M (1995) On-line map building and navigation for autonomous mobile robots, Proceedings of 1995 IEEE International Conference on Robotics and Automation, 21-27 May 1995,vol. 3, pp. 2900–2906

  20. Powell MJ, Zhao H, Ames AD (2012) Motion primitives for human-inspired bipedal robotic locomotion: walking and stair climbing. 2012 IEEE international conference on robotics and automation, pp. 543–549

  21. Rincon JA, Costa A, Novais P, et al. (2016) Detecting social emotions with a NAO Robot, advances in practical applications of scalable multi-agent system. The PAAMS Collection- 14th International Conference, Springer - Lecture Notes in Computer Science, Yves Demazeau et al. (eds), vol. 9662, pp. 286–289

  22. Ryberg A, Chistiansson A, Eriksson E, Lennartson B (2006) A new Camera Model for Higher Accuracy Pose Calculations. In: IEEE Proceedings of International Symposium on Industrial Electronics, 2798–2802

  23. Sari MP, Dewanto RS, Pramadihanto D (2019) Kinematic and dynamic modelling of "T-FLoW" humanoid robot for ascending and descending stair. 2019 International Electronics Symposium (IES), IEEE, 27–28

  24. Xu C, Wang Q, Chen H,  Mei S,  Du Liqiang (2017) Design and simulation of artificial limb picking robot based on somatosensory interaction, Transactions of the Chinese Society of Agricultural Engineering, 33(1):49–55(7)

  25. Wang J, Ramirez-Serrano A (2016) Stair-climbing and energy consumption evaluation of a leg-tracked quadruped robot. 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, 12–15

  26. Zhang Q, Fan CX, Yao T, Kamiya Y (2013) Action generation of a biped robot climbing stairs. 2013 IEEE International Conference on Mechatronics and Automation, IEEE, 4–7

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Acknowledgements

The authordeeply acknowledges Ms. Li, Feiwen  initial test support at first roughmodel.

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  1. Department of Electronic Engineering, Lunghwa University of Science and Technology, No.300,Sec.1,Wanshou Rd.Guishan District, Taoyuan City, 333326, Taiwan

    Li-Hong Juang

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Juang, LH. Humanoid robot runs up-down stairs using zero-moment with supporting polygons control. Multimed Tools Appl 82, 13275–13305 (2023). https://doi.org/10.1007/s11042-022-13723-0

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