Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T20:10:52.089Z Has data issue: false hasContentIssue false
  • English
  • Français

Adding low-cost passive toe joints to the feet structure of SURENA III humanoid robot

Published online by Cambridge University Press:  22 November 2016

Majid Sadedel ,
Aghil Yousefi-Koma ,
Majid Khadiv  and
Mohhamad Mahdavian
Majid Sadedel
Affiliation:
Center of Advanced Systems and Technologies (CAST), School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran. E-mails: [email protected], [email protected]
Aghil Yousefi-Koma*
Affiliation:
Center of Advanced Systems and Technologies (CAST), School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran. E-mails: [email protected], [email protected]
Majid Khadiv
Affiliation:
Center of Excellence in Robotics and Control, Advanced Robotics & Automated Systems (ARAS) Lab, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran. E-mail: [email protected]
Mohhamad Mahdavian
Affiliation:
Center of Advanced Systems and Technologies (CAST), School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Adding active toe joints to a humanoid robot structure has lots of difficulties such as mounting a small motor and an encoder on the robot feet. Conversely, adding passive toe joints is simple, since it only consists of a spring and a damper. Due to lots of benefits of implementing passive toe joints, mentioned in the literature, the goal of this study is to add passive toe joints to the SURENA III humanoid robot which was designed and fabricated at the Center of Advanced Systems and Technologies (CAST), University of Tehran. To this end, a simple passive toe joint is designed and fabricated, at first. Then, stiffness and damping coefficients are calculated using a vision-based measurement. Afterwards, a gait planning routine for humanoid robots equipped with passive toe joints is implemented. The tip-over stability of the gait is studied, considering the vibration of the passive toe joints in swing phases. The multi-body dynamics of the robot equipped with passive toe joints are presented using the Lagrange approach. Furthermore, system identification routine is adopted to model the dynamic behaviors of the power transmission system. By adding the calculated actuating torques for these two models, the whole dynamic model of the robot is computed. Finally, the performance of the proposed approach is evaluated by several simulations and experimental results. Results show that using passive toe joints reduces energy consumption of ankle and knee joints by 15.3% and 9.0%, respectively. Moreover, with relatively large values of stiffness coefficients, the required torque and power of the knee and hip joints during heel-off motion reduces as the ankle joint torque and power increases.

Keywords

Humanoid robotPassive toe jointsDynamic modelSystem identificationParametric analysis
Type
Articles
Information
Robotica , Volume 35 , Issue 11 , November 2017 , pp. 2099 - 2121
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Nishiwaki, K., Kagami, S., Kuniyoshi, Y., Inaba, M. and Inoue, H., “Toe Joints That Enhance Bipedal and Fullbody Motion of Humanoid Robots,” Proceedings of the ICRA'02, IEEE International Conference on Robotics and Automation, (IEEE), Washington, DC, USA (2002) pp. 3105–3110.Google Scholar
2. Buschmann, T., Lohmeier, S. and Ulbrich, H., “Humanoid robot lola: Design and walking control,” Physiol. Paris 103, 141–148 (2009).CrossRefGoogle ScholarPubMed
3. Tajima, R., Honda, D. and Suga, K., “Fast Running Experiments Involving a Humanoid Robot,” Proceedings of the ICRA'09, IEEE International Conference on Robotics and Automation, (IEEE), Kobe, Japan (2009) pp. 1571–1576.Google Scholar
4. Miura, K., Morisawa, M., Kanehiro, F., Kajita, S., Kaneko, K. and Yokoi, K., “Human-Like Walking with Toe Supporting for Humanoids,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (IEEE), San Francisco, CA, USA (2011) pp. 4428–4435.Google Scholar
5. Ogura, Y., Shimomura, K., Kondo, A., Morishima, A., Okubo, T., Momoki, S., Lim, H. and Takanishi, A., “Human-Like Walking with Knee Stretched, Heel-Contact and Toe-Off Motion by a Humanoid Robot,” IEEE/RSJ International Conference on Intelligent Robots and Systems, (IEEE), Beijing, China (2006) pp. 3976–3981.Google Scholar
6. Nelson, G., Saunders, A., Neville, N., Swilling, B., Bondaryk, J., Billings, D., Lee, C., Playter, R. and Raibert, M., “Petman: A humanoid robot for testing chemical protective clothing,” Robot. Soc. Japan 30, 372–377 (2012).Google Scholar
7. Kajita, S., Kaneko, K., Morisawa, M., Nakaoka, S. and Hirukawa, H., “Zmp-Based Biped Running Enhanced by Toe Springs,” IEEE International Conference on Robotics and Automation, (IEEE), Roma, Italy (2007) pp. 3963–3969.Google Scholar
8. Doubliez, P., Bruneau, O. and Ouezdou, F., “Force control of the redundant leg of a biped robot to produce large inertial effects for crossing obstacles,” Int. J. Humanoid Robot. 9, 125–130 (2012).CrossRefGoogle Scholar
9. Ki, A., Cheol, L. and Jo, G., “Development of a Biped Robot with Toes to Improve Gait Pattern,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, (IEEE), Kobe, Japan (2003) pp. 729–734.Google Scholar
10. Wang, L., Yu, Z., Meng, Q. and Zhang, Z., “Influence Analysis of Toe-Joint on Biped Gaits,” IEEE International Conference on Mechatronics and Automation, (IEEE), Luoyang, Henan, China (2006) pp. 1631–1635.Google Scholar
11. Kouchaki, E. and Sadigh, M. J., “Effect of Toe-Joint Bending on Biped Gait Performance,” IEEE International Conference on Robotics and Biomimetics (ROBIO), (IEEE), Tianjin, China (2010) pp. 697–702.Google Scholar
12. Handharu, N., Yoon, J. and Kim, G., “Gait Pattern Generation with Knee Stretch Motion for Biped Robot Using Toe and Heel Joints,” 8th IEEE-RAS International Conference on Humanoids, (IEEE), Daejeon, South Korea (2008) pp. 265–270.Google Scholar
13. Sadigh, M. J., Kouchaki, E. and Wu, C., “Effects of constraints on standing balance of a biped with toe-joints,” Int. J. Humanoid Robot. 9, 125–136 (2012).Google Scholar
14. Sellaouti, R., Stasse, O., Kajita, S., Yokoi, K. and Kheddar, A., “Faster and Smoother Walking of Humanoid Hrp-2 with Passive Toe Joints,” IEEE/RSJ International Conference on Intelligent Robots and Systems, (IEEE), Daejeon, South Korea (2006) pp. 4909–4914.Google Scholar
15. Zhu, J., Wang, Q. and Wang, L., “Effects of Toe stiffness on ankle kinetics in a robotic transtibial prosthesis during level-ground walking,” Mechatronics 24, 1254–1261 (2014).Google Scholar
16. Sun, S., Huang, Y. and Wang, Q., “Adding adaptable toe stiffness affects energetic efficiency and dynamic behaviors of bipedal walking,” J. Theoretical Biol. 388, 108–118 (2016).Google Scholar
17. Kumar, R., Handharu, N., Jungwon, Y. and Gap-soon, K., “Hybrid Toe and Heel Joints for Biped/Humanoid Robots for Natural Gait,” Proceeding of the ICCAS'07, International Conference on Control, Automation and Systems, (ICCAS), Seoul, South Korea (2007) pp. 2687–2692.Google Scholar
18. Yamamoto, K., Sugihara, T. and Nakamura, Y., “Toe Joint Mechanism Using Parallel Four-Bar Linkage Enabling Humanlike Multiple Support at Toe Pad and Toe Tip,” 7th IEEE-RAS International Conference on Humanoid Robots, (IEEE), Pittsburg, PA, USA (2007) pp. 410–415.Google Scholar
19. Borovac, B. and Slavnic, S., “Design of Multi-Segment Humanoid Robot Foot,” Research and Education in Robotics (Springer, Berlin, Heidelberg, 2009), pp. 12–18.Google Scholar
20. Narioka, K., Homma, T. and Hosoda, K., “Humanlike Ankle-Foot Complex for a Biped Robot,” IEEE-RAS/RSJ International Conference on Humanoid Robots, (IEEE), Osaka, Japan (2012) pp. 117–125.Google Scholar
21. Vukobratović, M. and Borovac, B., “Zero-moment point-thirty five years of its life,” Int. J. Humanoid Robot. 1, 157–173 (2004).Google Scholar
22. Goswami, A., “Postural stability of biped robots and the foot-rotation indicator (Fri) point,” Int. J. Robot. Res. 18, 523–533 (1999).Google Scholar
23. Popovic, M. B., Goswami, A. and Herr, H., “Ground reference points in legged locomotion: Definitions, biological trajectories and control implications,” Int. J. Robot. Res. 24, 1013–1032 (2005).CrossRefGoogle Scholar
24. Hirukawa, H., Hattori, S., Kajita, S., Harada, K., Kaneko, K., Kanehiro, F., Morisawa, M. and Nakaoka, S., “A Pattern Generator of Humanoid Robots Walking on a Rough Terrain,” IEEE International Conference on Robotics and Automation, (IEEE), Roma, Italy (2007) pp. 2181–2187.Google Scholar
25. He, W., Dong, Y. and Sun, C., “Adaptive Neural Impedance Control of a Robotic Manipulator with Input Saturation,” IEEE Trans. Syst. Man Cybern.: Syst. 46, 334–344 (2016).Google Scholar
26. He, W., David, A.O., Yin, Z. and Sun, C., “Neural Network Control of a Robotic Manipulator with Input Deadzone and Output Constraint,” IEEE Trans. Syst. Man Cybern.: Syst. 99, 1–12 (2015).Google Scholar
27. He, W., Chen, Y. and Yin, Z., “Adaptive neural network control of an uncertain robot with full-state constraints,” IEEE Trans. Cybern. 46, 620–629 (2016).Google Scholar
28. Zhang, Z., Li, Z., Zhang, Y., Luo, Y. and Li, Y., “Neural-dynamic-method-based dual-arm cmg scheme with time-varying constraints applied to humanoid robots,” IEEE Trans. Neural Netw. Learning Syst. 26, 3251–3262 (2015).Google Scholar
29. Carbone, G., Ogura, Y., Lim, H., Takanishi, A. and Ceccarelli, M., “Dynamic simulation and experiments for the design of a new 7-dofs biped walking leg module,” Robotica 22, 41–50 (2004).Google Scholar
30. Channon, P., Hopkins, S. and Pham, D., “Derivation of optimal walking motions for a bipedal walking robot,” Robotica 10, 165–172 (1992).Google Scholar
31. Chevallereau, C. and Aoustin, Y., “Optimal reference trajectories for walking and running of a biped robot,” Robotica 19, 557–569 (2001).Google Scholar
32. Rostami, M. and Bessonnet, G., “Sagittal gait of a biped robot during the single support phase. part 2: Optimal motion,” Robotica 19, 241–253 (2001).Google Scholar
33. Saidouni, T. and Bessonnet, G., “Generating globally optimised sagittal gait cycles of a biped robot,” Robotica 21, 199–210 (2003).Google Scholar
34. Sadigh, M. J. and Mansouri, S., “Application of phase-plane method in generating minimum time solution for stable walking of biped robot with specified pattern of motion,” Robotica 31, 837–851 (2013).Google Scholar
35. Zhou, X., Guan, Y., Jiang, L., Zhu, H., Cai, C., Wu, W. and Zhang, H., “Stability of biped robotic walking with frictional constraints,” Robotica 31, 573–588 (2013).Google Scholar
36. Khadiv, M., Moosavian, S.A.A., Yousefi-Koma, A., Sadedel, M. and Mansouri, S., “Optimal gait planning for humanoids with 3d structure walking on slippery surfaces,” Robotica, 1–19 (2015).Google Scholar
37. Aoi, S. and Tsuchiya, K., “Generation of bipedal walking through interactions among the robot dynamics, the oscillator dynamics, and the environment: Stability characteristics of a five-link planar biped robot,” Auton. Robots 30, 123–141 (2011).Google Scholar
38. Sadedel, M., Yousefi-Koma, A. and Khadiv, M., “Offline Path Planning, Dynamic Modeling and Gait Optimization of a 2d Humanoid Robot,” 2nd RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), (IEEE), Tehran, Iran (2014) pp. 131–136.Google Scholar
39. Khadiv, M., Moosavian, S. A. and Sadedel, M., “Dynamics Modeling of Fully-Actuated Humanoids with General Robot-Environment Interaction,” 2nd RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), (IEEE), Tehran, Iran (2014) pp. 233–238.Google Scholar
40. Ugurlu, B., Saglia, J., Tsagarakis, N. and Caldwell, D., “Yaw moment compensation for bipedal robots via intrinsic angular momentum constraint,” Int. J. Humanoid Robot. 9, 1250033 (2012).Google Scholar
41. Wang, T., Chevallereau, C. and Rengifo, C., “Walking and steering control for a 3d biped robot considering ground contact and stability,” Robot. Auton. Syst. 60, 962–977 (2012).Google Scholar
42. Aoustin, Y. and Formalskii, A., “3d walking biped: Optimal swing of the arms,” Multibody Syst. Dyn. 32, 55–66 (2014).Google Scholar
43. Sadedel, M., Yousefikoma, A. and Iranmanesh, F., “Analytical dynamic modelling of heel-off and toe-off motions for a 2d humanoid robot,” J. Comput. Appl. Mech. 46, 243–256 (2015).Google Scholar
44. Inman, D., Engineering Vibration (3rd). (Pearson, ISBN 0-13-228173-2, Upper Saddle River, NJ, 2007).Google Scholar
45. Nunez, J., Briseno, A., Rodriguez, D., Ibarra, J. and Rodriguez, V., “Explicit Analytic Solution for Inverse Kinematics of Bioloid Humanoid Robot,” Robotics Symposium and Latin American Robotics Symposium (SBR-LARS), (IEEE), Washington, DC, USA (2012) pp. 33–38.Google Scholar
46. Huang, Q. and Nakamura, Y., “Sensory reflex control for humanoid walking,” IEEE Trans. Robot. 21, 977–984 (2005).CrossRefGoogle Scholar
47. Baruh, H., Analytical Dynamics, (WCB/McGraw-Hill, Boston, 1999).Google Scholar
48. Ben, A. and Greville, T., Generalized Inverses: Theory and Applications, vol. 15 (Springer Science & Business Media, New York, USA, 2003).Google Scholar
49. Grcar, J., “How ordinary elimination became gaussian elimination,” Historia Math. 38, 163–218 (2011).Google Scholar
50. Taghirad, H. and Belanger, P., “Modeling and parameter identification of harmonic drive systems,” J. Dyn. Syst. Meas. Control Trans.-Am. Soc. Mech. Eng. 120, 439–444 (1998).Google Scholar