Advertisement

Cluster Computing

, Volume 22, Supplement 6, pp 13041–13053 | Cite as

Turning strategy of snake-like robot based on serpenoid curve under cloud assisted smart conditions

  • Chao Wang
  • Yanbin Peng
  • Dongfang Li
  • Zhenhua Pan
  • Hongbin DengEmail author
  • Dongguang Li
  • Bin Li
Article
  • 220 Downloads

Abstract

This paper presents a turning strategy of snake-like robot based on Serpenoid curve. By using four criteria to judge turning control approaches on the basis of serpenoid curve, three commonly used turning control approaches (i.e., central value modulation method, phase modulation method and amplitude modulation method) are first analyzed. Then the tangent control approach and the combination control approach are used to solve turning challenges such as deficiency in maintaining the serpenoid curve, limited turning angle, discontinuous joint angle, large turning radius and large amplitude of joint angle variation during and after turning. These approaches were tested and verified by using a snake-like robot prototype. It is found that the proposed approaches work well for the turning locomotion of the snake-like robot. The present turning strategy provides an important alternative for locomotion control of the snake-like robot.

Keywords

Snake-like robot Serpenoid curve Turning strategy Control approach 

Notes

Acknowledgements

This work was supported by the national defense basic research and development plan of the assembly support.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. 1.
    Endo, G., Togawa, K., Hirose, S.: Study on self-contained and terrain adaptive active cord mechanism. In: IEEE International Conference on Intelligent Robots and Systems, pp. 1399–1405 (1999)Google Scholar
  2. 2.
    Komura, H., Yamada, H., Hirose, S.: Development of snake-like robot ACM-R8 with large and mono-tread wheel. Adv. Robot. 29(17), 1081–1094 (2015)CrossRefGoogle Scholar
  3. 3.
    Yamada, H., Hirose, S.: Study on the 3D shape of active cord mechanism. In: Proceedings of the IEEE International Conference on robotics and Automation, pp. 2890–2895. Orlando, USA (2006)Google Scholar
  4. 4.
    Liljeback, P., Pettersen, K.Y., Stavdahl, O., et al.: Snake Robots Modelling, Mechatronics and Control. National Defense Industry Press, Beijing (2015)zbMATHGoogle Scholar
  5. 5.
    Zhao, T., Li, N.: Nominal mechanism method of dynamic modeling for snake-like robots. Chin. J. Mech. Eng. 43(8), 66–71 (2007)CrossRefGoogle Scholar
  6. 6.
    Tao, M., Ota, K., Dong, M.: Foud: integrating fog and cloud for 5G-enabled V2G networks. IEEE Netw. 31(2), 8–13 (2017)CrossRefGoogle Scholar
  7. 7.
    Sudipta, S., Bose, R., Sarddar, D.: Server utilization-based smart temperature monitoring system for cloud data center. Industry Interactive Innovations in Science, Engineering and Technology, pp. 309–319. Springer, Singapore (2018)Google Scholar
  8. 8.
    Bi, C., Wang, H., Bao, R.: SAR image change detection using regularized dictionary learning and fuzzy clustering. In: 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 327–330. IEEE (2014, November)Google Scholar
  9. 9.
    Liu, Q., et al.: Green data center with IoT sensing and cloud-assisted smart temperature control system. Comput. Netw. 101, 104–112 (2016)CrossRefGoogle Scholar
  10. 10.
    Wang, S., et al.: Cloud-assisted interaction and negotiation of industrial robots for the smart factory. Comput. Electr. Eng. (2017)Google Scholar
  11. 11.
    Xu, X., et al.: Health monitoring and management for manufacturing workers in adverse working conditions. J. Med. Syst. 40(10), 222 (2016)CrossRefGoogle Scholar
  12. 12.
    Sarkar, Manash, et al.: Configuring a trusted cloud service model for smart city exploration using hybrid intelligence. Int. J. Ambient Comput. Intell. (IJACI) 8(3), 1–21 (2017)CrossRefGoogle Scholar
  13. 13.
    Botta, A., et al.: Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2016)CrossRefGoogle Scholar
  14. 14.
    Bakshi, S., et al.: Fast periocular authentication in handheld devices with reduced phase intensive local pattern. Multimed. Tools Appl. 1–29 (2017)Google Scholar
  15. 15.
    Wang, H., Wang, J.: An effective image representation method using kernel classification. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 853–858. IEEE (2014 November)Google Scholar
  16. 16.
    Neville, R.F., Gupta, S., Kuraguntla, D.: Initial in-vitro and in-vivo evaluation of a self-monitoring “smart” bypass graft. J. Vasc. Surg. 63(1), 294–295 (2016)CrossRefGoogle Scholar
  17. 17.
    Sangaiah, A.K., Samuel, O.W., Li, X., Abdel-Basset, M., Wang, H.: Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm. Comput. Electr. Eng. (2017)Google Scholar
  18. 18.
    Changlon, Y., Shugen, M., Bin, L., et al.: Study on turning and sidewise motion of a snake-like robot. Chin. J. Mech. Eng. 40(10), 119–123 (2004)CrossRefGoogle Scholar
  19. 19.
    Zhou, C., Low, K.H.: Design and locomotion control of a biomimetic underwater vehicle with fin propulsion. IEEE/ASME Trans. Mechatron. 17(1), 25–35 (2012)CrossRefGoogle Scholar
  20. 20.
    Li, B., Ye, C.: Study on autonomous control of the snake-like robot movement on a plane. Ghin. High Technol. Lett. 15(2) (2005)Google Scholar
  21. 21.
    Hirose, S.: Biologically Inspired Robots—Snake-like Locomotors and Manipulators. Oxford University Press, Oxford (1993)Google Scholar
  22. 22.
    Ma, S.G., Hiroaki, A., Li, L.: Development of a creeping snake-robot. Int. J. Robot. Autom. 17(4), 146–153 (2002)Google Scholar
  23. 23.
    Hirose, S., Yamada, H.: Snake-like robots: machine design of biologically inspired robots. IEEE Robot. Autom. Mag. 16(1), 88–98 (2009)CrossRefGoogle Scholar
  24. 24.
    Nor, N.M., Ma, S.: CPG-based locomotion control of a snake-like robot for obstacle avoidance. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 347–352. Hong Kong (2014)Google Scholar
  25. 25.
    Wu, X.: CPG-Based Neural Controller for Serpentine Locomotion of a Snake-Like Robot, Doctoral Dissertation, Science and Engineering. Ritsumeikan University, Kyoto (2011)Google Scholar
  26. 26.
    Wu, X., Ma, S.: Autonomous collision-free behavior of a snakelike robot. In: Proceedings of the 2010 IEEE International Conference on Robotics and Biomimetics. Tianjin, China (2010)Google Scholar
  27. 27.
    Wang, C., Deng, H.b., Xia, F., Li, Y.: Study on climbing locomotion mechanism of snake robot with universal unit. In: 2016 8th International Conference on Modelling, Identification and Control (ICMIC). pp. 460–465, Algiers (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Chao Wang
    • 1
    • 2
  • Yanbin Peng
    • 3
  • Dongfang Li
    • 1
  • Zhenhua Pan
    • 1
  • Hongbin Deng
    • 1
    Email author
  • Dongguang Li
    • 1
  • Bin Li
    • 2
  1. 1.School of Mechatronical EngineeringBeijing Institute of TechnologyBeijingChina
  2. 2.China North Industries Corp.BeijingChina
  3. 3.Beijing Institute of Aerospace Control DecicesBeijngChina

Personalised recommendations