Advertisement

From Quasi-static to Kinodynamic Planning for Spherical Tensegrity Locomotion

  • Zakary Littlefield
  • David Surovik
  • Weifu Wang
  • Kostas E. BekrisEmail author
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

Tensegrity-based robots can achieve locomotion through shape deformation and compliance. They are highly adaptable to their surroundings, have light weight, low cost and high endurance. Their high dimensionality and highly dynamic nature, however, complicate motion planning. So far, only rudimentary quasi-static solutions have been achieved, which do not utilize tensegrity dynamics. This work explores a spectrum of planning methods that increasingly allow dynamic motion for such platforms. Symmetries are first identified for a prototypical spherical tensegrity robot, which reduce the number of needed gaits. Then, a numerical process is proposed for generating quasi-static gaits that move forward the system’s center of mass in different directions. These gaits are combined with a search method to achieve a quasi-static solution. In complex environments, however, this approach is not able to fully explore the space and utilize dynamics. This motivates the application of sampling-based, kinodynamic planners. This paper proposes such a method for tensegrity locomotion that is informed and has anytime properties. The proposed solution allows the generation of dynamic motion and provides good quality solutions. Evaluation using a physics-based model for the prototypical robot highlight the benefits of the proposed scheme and the limits of quasi-static solutions.

References

  1. 1.
  2. 2.
    Aldrich, J.B., Skelton, R.E., Delgado, K.K.: Control synthesis for light and agile robotic tensegrity structures. In: ACC (2003)Google Scholar
  3. 3.
    Bliss, T., Iwasaki, T., Bart-Smith, H.: Central pattern generator control of a tensegrity swimmer. Trans. Mech. 18(2) (2013)Google Scholar
  4. 4.
    Bohm, V., Zimmermann, K.: Vibration-driven mobile robots based on single actuated tensegrity structures. In: ICRA (2013).  https://doi.org/10.1109/ICRA.2013.6631362
  5. 5.
    Bruce, J., Caluwaerts, K., Iscen, A., Sabelhaus, A.P., SunSpiral, V.: Design and evolution of a modular tensegrity robot platform. In: ICRA, pp. 3483–3489 (2014)Google Scholar
  6. 6.
    Caluwaerts, K., Despraz, J., Işçen, A., Sabelhaus, A.P., Bruce, J., et al.: Design and control of compliant tensegrity robots through simulation and hardware validation. JRSI 11(98) (2014).  https://doi.org/10.1098/rsif.2014.0520
  7. 7.
    Friesen, J., Pogue, A., Bewley, T., de Oliveira, M.C., Skelton, R.E., SunSpiral, V.: DuCTT: a tensegrity robot for exploring duct systems. In: ICRA, pp. 4222–4228 (2014).  https://doi.org/10.1109/ICRA.2014.6907473
  8. 8.
    Furuya, H.: Concept of deployable tensegrity structures in space application. IJSS 7(143–151), (1992)Google Scholar
  9. 9.
    Heartney, E.: Kenneth snelson: forces made visible, 2009 edn. Hudson Hills (2009)Google Scholar
  10. 10.
    Hernández Juan, S., Skelton, R.E., Mirats Tur, J.M.: Dynamically stable collision avoidance for tensegrity robots. In: ReMAR (2009)Google Scholar
  11. 11.
    Hsu, D., Kindel, R., Latombe, J.C., Rock, S.: Randomized kinodynamic motion planning with moving obstacles. Int. J. Robot. Res. (IJRR) 21, 233–255 (2002)CrossRefGoogle Scholar
  12. 12.
    Ingber, D.E.: Architecture of Life. Scientific American (1998)Google Scholar
  13. 13.
    Iscen, A., Agogino, A., SunSpiral, V., Tumer, K.: Flop and roll: learning robust goal-directed locomotion for a tensegrity robot. In: IROS, pp. 2236–2243 (2014)Google Scholar
  14. 14.
    Kim, K., Agogino, A.K., Toghyan, A., Moon, D., Taneja, L., Agogino, A.M.: Robust learning of tensegrity robot control for locomotion through form-finding. In: IROS (2015)Google Scholar
  15. 15.
    Kunz, T., Stilman, M.: Kinodynamic RRTS with fixed time step and best-input extension are not probabilistically complete. In: WAFR, pp. 233–244 (2014)Google Scholar
  16. 16.
    LaValle, S.M., Kuffner Jr., J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)CrossRefGoogle Scholar
  17. 17.
    Levin, S.: The tensegrity-truss as a model for spine mechanics: biotensegrity. JMMB 2, 375–388 (2002)Google Scholar
  18. 18.
    Li, Y., Littlefield, Z., Bekris, K.E.: Asymptotically optimal sampling-based kinodynamic planning. Int. J. Robot. Res. 35(5), 528–564 (2016)CrossRefGoogle Scholar
  19. 19.
    Littlefield, Z., Bekris, K.E.: Informed asymptotically near-optimal planning for field robots with dynamics. In: Conference on Field and Service Robotics (FSR) (2017)Google Scholar
  20. 20.
    Littlefield, Z., Caluwaerts, K., Bruce, J., SunSpiral, V., Bekris, K.E.: Integrating simulated tensegrity models with efficient motion planning for planetary navigation. In: i-SAIRAS (2016)Google Scholar
  21. 21.
    Mirats Tur, J.M., Camps, J.: A three-DoF actuated robot. IEEE RAM 18(3), 96–103 (2011).  https://doi.org/10.1109/MRA.2011.940991CrossRefGoogle Scholar
  22. 22.
    Mirletz, B., Bhandal, P., Adams, R.D., Agogino, A.K., Quinn, R.D., SunSpiral, V.: Goal directed CPG based control for high DOF tensegrity spines traversing irregular terrain. Soft Robot. (2015)Google Scholar
  23. 23.
    Mirletz, B.T., Park, I.W., Quinn, R.D., SunSpiral, V.: Towards bridging the reality gap between tensegrity simulation and robotic hardware. In: IROS (2015)Google Scholar
  24. 24.
    Paul, C., Valero-Cuevas, F.J., Lipson, H.: Design and control of tensegrity robots for locomotion. TRO 22(5) (2006).  https://doi.org/10.1109/TRO.2006.878980
  25. 25.
    Pinaud, J.P., Masic, M., Skelton, R.E.: Path planning for the deployment of tensegrity structures. In: SPIE ISSSM (2003)Google Scholar
  26. 26.
    Porta, J.M., Hernández Juan, S.: Path planning for active tensegrity structures. IJSS 78–79, 47–56 (2016).  https://doi.org/10.1016/j.ijsolstr.2015.09.018, http://www.sciencedirect.com/science/article/pii/S0020768315004035
  27. 27.
    Rhode-Barbarigos, L., Schulin, C., Ali, N.B.H., Motro, R., Smith, I.F.C.: Mechanism-based approach for the deployment of a tensegrity ring-module. JSE, 539–548 (2012)Google Scholar
  28. 28.
    Rovira, A.G., Mirats Tur, J.M.: Control and simulation of a tensegrity-based mobile robot. RAS 57(5), 526–535 (2009)Google Scholar
  29. 29.
    Sabelhaus, A.P., Bruce, J., Caluwaerts, K., Manovi, P., Firoozi, R.F., Dobi, S., et al.: System design and locomotion of SUPERball, an untethered tensegrity robot. In: ICRA, pp. 2867–2873. IEEE (2015)Google Scholar
  30. 30.
    Shibata, M., Saijyo, F., Hirai, S.: Crawling by body deformation of tensegrity structure robots. In: ICRA (2009)Google Scholar
  31. 31.
    Skelton, R.E., de Oliveira, M.C.: Tensegrity Systems (2009)Google Scholar
  32. 32.
    Skelton, R.E., Sultan, C.: Controllable tensegrity: a new class of smart structures. In: Proceedings of the SPIE (1997)Google Scholar
  33. 33.
    van de Wijdeven, J., de Jager, A.: Shape change of tensegrity structures: design and control. In: ACC (2005)Google Scholar
  34. 34.
    Wroldsen, A.S., de Oliveira, M.C., Skelton, R.E.: A discussion on control of tensegrity systems. In: CDC (2006)Google Scholar
  35. 35.
    Xu, X., Sun, F., Luo, Y., Xu, Y.: Collision-free path planning of tensegrity structures. JSE (2013)Google Scholar
  36. 36.
    Zimmermann, K., Zeidis, I., Behn, C.: Mechanics of Terrestrial Locomotion. Springer, Berlin (2009)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zakary Littlefield
    • 1
  • David Surovik
    • 1
  • Weifu Wang
    • 2
  • Kostas E. Bekris
    • 1
    Email author
  1. 1.Department of Computer ScienceRutgers UniversityPiscatawayUSA
  2. 2.Department of Computer EngineeringUniversity of Albany, SUNYAlbanyUSA

Personalised recommendations