Advertisement

Modular Neural Control for Object Transportation of a Bio-inspired Hexapod Robot

  • Chris Tryk Lund Sørensen
  • Poramate Manoonpong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9825)

Abstract

Insects, like dung beetles, can perform versatile motor behaviors including walking, climbing an object (i.e., dung ball), as well as manipulating and transporting it. To achieve such complex behaviors for artificial legged systems, we present here modular neural control of a bio-inspired hexapod robot. The controller utilizes discrete-time neurodynamics and consists of seven modules based on three generic neural networks. One is a neural oscillator network serving as a central pattern generator (CPG) which generates basic rhythmic patterns. The other two networks are so-called velocity regulating and phase switching networks. They are used for regulating the rhythmic patterns and changing their phase. As a result, the modular neural control enables the hexapod robot to walk and climb a large cylinder object with a diameter of 18 cm (i.e., \(\approx 2.8\) times the robot’s body height). Additionally, it can also generate different hind leg movements for different object manipulation modes, like soft and hard pushing. Combining these pushing modes, the robot can quickly transport the object across an obstacle with a height up to 10 cm (i.e., \(\approx 1.5\) times the robot’s body height). The controller was developed and evaluated using a physical simulation environment.

Keywords

Object manipulation Locomotion Modular neural network Central pattern generator Walking machines Autonomous robots 

Notes

Acknowledgments

We would like to thank Georg Martius for technical advise about the LpzRobots simulation software.

References

  1. 1.
    Cully, A., Clune, J., Tarapore, D., Mouret, J.B.: Robots that can adapt like animals. Nature 521, 503–507 (2015)CrossRefGoogle Scholar
  2. 2.
    Inoue, K., Fujii, S., Takubo, T., Mae, Y., Arai, T.: Ladder climbing method for the limb mechanism robot asterisk. Adv. Robot. 24, 1557–1576 (2010)CrossRefGoogle Scholar
  3. 3.
    Crespi, A., Karakasiliotis, K., Guignard, A., Ijspeert, A.J.: Salamandra robotica II: an amphibious robot to study salamander-like swimming and walking gaits. IEEE Trans. Robot. 29, 308–320 (2013)CrossRefGoogle Scholar
  4. 4.
    Bartsch, S., Planthaber, S.: Scarabaeus: a walking robot applicable to sample return missions. In: Gottscheber, A., Enderle, S., Obdrzalek, D. (eds.) EUROBOT 2008. CCIS, vol. 33, pp. 128–133. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Rehman, B.U., Focchi, M., Frigerio, M., Goldsmith, J., Caldwell, D.G., Semini, C.: Design of a hydraulically actuated arm for a quadruped robot. In: Proceedings of the International Conference on Climbing and Walking Robots, pp. 283–290 (2015)Google Scholar
  6. 6.
    Heppner, G., Buettner, T., Roennau, A., Dillmann, R.: Versatile - high power gripper for a six legged walking robot. In: Proceedings of the International Conference on Climbing and Walking Robots, pp. 461–468 (2014)Google Scholar
  7. 7.
    Koyachi, N., Adachi, H., Arai, T., Izumi, M., Hirose, T., Senjo, N., Murata, R.: Walk and manipulation by a hexapod with integrated limb mechanism of leg and arm. J. Robot. Soc. Jpn. 22, 411–421 (2004)CrossRefGoogle Scholar
  8. 8.
    Inoue, K., Ooe, K., Lee, S.: Pushing methods for working six-legged robots capable of locomotion and manipulation in three modes. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4742–4748 (2010)Google Scholar
  9. 9.
    Takeo, G., Takubo, T., Ohara, K., Mae, Y., Arai, T.: Internal force control for rolling operation of polygonal prism. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics, pp. 586–591 (2009)Google Scholar
  10. 10.
    Philips, T.K., Pretorius, E., Scholtz, C.H.: A phylogenetic analysis of dung beetles (Scarabaeinae): unrolling an evolutionary history. Invertebr. Syst. 18, 53–88 (2004)CrossRefGoogle Scholar
  11. 11.
    Bässler, U., Büschges, A.: Pattern generation for stick insect walking movements-multisensory control of a locomotor program. Brain Res. Rev. 27, 65–88 (1998)CrossRefGoogle Scholar
  12. 12.
    Valsalam, V., Miikkulainen, R.: Modular neuroevolution for multilegged locomotion. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 265–272 (2008)Google Scholar
  13. 13.
    Hornby, G., Takamura, S., Yamamoto, T., Fujita, M.: Autonomous evolution of dynamic gaits with two quadruped robots. IEEE Trans. Robot. Autom. 21, 402–410 (2005)CrossRefGoogle Scholar
  14. 14.
    Manoonpong, P., Wörgötter, F., Laksanacharoen, P.: Biologically inspired modular neural control for a leg-wheel hybrid robot. Adv. Robot. Res. 1, 101–126 (2014)CrossRefGoogle Scholar
  15. 15.
    Manoonpong, P., Pasemann, F., Wörgötter, F.: Sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines. Robot. Auton. Syst. 56, 265–288 (2008)CrossRefGoogle Scholar
  16. 16.
    Grinke, E., Tetzlaff, C., Wörgötter, F., Manoonpong, P.: Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot. Front. Neurorobot. 9, 1–15 (2015). doi: 10.3389/fnbot.2015.00011 CrossRefGoogle Scholar
  17. 17.
    Pasemann, F., Hild, M., Zahedi, K.: So(2)-networks as neural oscillators. In: Proceedings of 7th International Work-Conference on Artificial and Natural Neural Networks (IWANN 2003), pp. 1042–1042 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Chris Tryk Lund Sørensen
    • 1
  • Poramate Manoonpong
    • 1
  1. 1.Embodied AI and Neurorobotics Lab, Centre for BioRobotics, Mærsk Mc-Kinney Møller InstituteUniversity of Southern DenmarkOdense MDenmark

Personalised recommendations