Synergistic control of a multi-segments vertebral column robot based on tensegrity for postural balance

References

• Pfeifer R, Lungarella M, Iida M. Self-organization, embodiment, and biologically inspired robotics. Science. 2007;318:1088–1093. doi: 10.1126/science.1145803
   PubMed Web of Science ®Google Scholar
• McEvoy M, Correll N. Materials that couple sensing, actuation, computation, and communication. Science. 2007;318:1088–1093. doi: 10.1126/science.1145803
   PubMed Web of Science ®Google Scholar
• Bernstein N. The coordination and regulation of movements. Oxford: Pergamon Press; 1967.
   Google Scholar
• Todorov E. Optimality principles in sensorimotor control. Nat Neurosci. 2004;7(9):907–915. doi: 10.1038/nn1309
   PubMed Web of Science ®Google Scholar
• Bizzi E, Giszter S, Loeb E, et al. Modular organization of motor behavior in the frog's spinal cord. Trends Neurosci. 1995;18:442–445. doi: 10.1016/0166-2236(95)94494-P
   PubMed Web of Science ®Google Scholar
• Kelso JS. Dynamic patterns: the self-organization of brain and behavior. Cambridge, MA: MIT Press; 1995.
   Google Scholar
• Kelso JS, Haken H. Synergetics of brain and behavior. In: Murphy MP, O'Neill LAJ, editors. What is life? The next fifty years. Cambridge: Cambridge University Press; 1995. p. 137–160.
   Google Scholar
• Bardy B, Oullier O, Bootsma R, et al. The dynamics of human postural transitions. J Exper Psychol Hum Percept Perform. 2002;28:499–514. doi: 10.1037/0096-1523.28.3.499
   PubMed Web of Science ®Google Scholar
• Ly O, Lapeyre M, Oudeyer P. Bio-inspired vertebral column, compliance and semi-passive dynamics in a lightweight robot. International Conference on Robots and Systems; San Francisco. 2011. p. 1–8.
   Google Scholar
• Nakajima K, Hauser H, Kang R, et al. A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm. Front Comput Neurosci. 2013;7:91. doi: 10.3389/fncom.2013.00091
   PubMed Web of Science ®Google Scholar
• Zhao Q, Sumioka H, Nakajima K, et al. Spine as an engine: effect of spine morphology on spine-driven quadruped locomotion. Adv Robot. 2014;28(6):367–378. doi: 10.1080/01691864.2013.867287
   Web of Science ®Google Scholar
• Wei X, Wang C, Long Y, et al. The effect of spine on the bounding dynamic performance of legged system. Adv Robot. 2015;29(15):973–987. doi: 10.1080/01691864.2015.1015442
   Web of Science ®Google Scholar
• Flemons T. The bones of tensegrity. http://wwwintensiondesignscom/bones_of_tensegrity. 2012
   Google Scholar
• Bizzi FEClarac. Motor systems. Curr Opin Neurobiol. 1999;9(6):659–662. doi: 10.1016/S0959-4388(99)00042-2
   PubMed Web of Science ®Google Scholar
• Marder E, Calabrese R. Principles of rhythmic motor pattern production. Physiol Rev. 1996;76:687–717. doi: 10.1152/physrev.1996.76.3.687
   PubMed Web of Science ®Google Scholar
• Calabrese R. Oscillation in motor pattern-generating networks. Curr Opin Neurobiol. 1995;5:816–823. doi: 10.1016/0959-4388(95)80111-1
   PubMed Web of Science ®Google Scholar
• Bardy B, Marin L, Stoffregen T, et al. Postural coordination modes considered as emergent phenomena. Exp Psychol Hum Percept Perform. 1999;25:1284–1301. doi: 10.1037/0096-1523.25.5.1284
   PubMed Web of Science ®Google Scholar
• Bonnet V, Ramdani S, Fraisse P, et al. A structurally optimal control model for predicting and analyzing human postural coordination. J Biomech. 2011;44:2123–2128. doi: 10.1016/j.jbiomech.2011.05.027
   PubMed Web of Science ®Google Scholar
• Taga G. A model of the neuro-musculo-skeletal system for human locomotion. i. emergence of basic gait. Biol Cybern. 1995;73(2):97–111. doi: 10.1007/BF00204048
   PubMed Web of Science ®Google Scholar
• Der R, Hesse F, Martius G. Learning to feel the physics of a body. Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 06); Washington, DC: IEEE Computer Society. 2005. p. 252–257.
   Google Scholar
• Fuller RB. Synergetics: explorations in the geometry of thinking. New York (NY): MacMillan Publishing Company; 1975
   Google Scholar
• Snelson K. Continuous tension, discontinuous compression structures. United States patent 3169611. 1965
   Google Scholar
• Paul C, Valero-Cuevas F, Lipson H. Design and control of tensegrity robots for locomotion. IEEE Trans Robot. 2006;22(5):878–980. doi: 10.1109/TRO.2006.878980
   Web of Science ®Google Scholar
• Bliss T, Werly J, Iwasaki T, et al. Experimental validation of robust resonance entrainment for cpg-controlled tensegrity structures. IEEE Trans Control Syst Technol. 2008;21:666–678. doi: 10.1109/TCST.2012.2189400
   Web of Science ®Google Scholar
• Tietz B, Carnahan R, Bachmann R, et al. Tetraspine: Robust terrain handling on a tensegrity robot using central pattern generators. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM); Wollongong, Australia. 2013. p. 261–267.
   Google Scholar
• Hauser H, Ijspeert A, Fchslin R, et al. Towards a theoretical foundation for morphological computation with compliant bodies. Biol Cybern. 2011;105(5–6):355–370. doi: 10.1007/s00422-012-0471-0
   PubMed Web of Science ®Google Scholar
• Hauser H, Ijspeert A, Fchslin R, et al. The role of feedback in morphological computation with compliant bodies. Biol Cybern. 2012;106(10):595–613. doi: 10.1007/s00422-012-0516-4
   PubMed Web of Science ®Google Scholar
• Caluwaerts K, Despraz J, Sabelhaus A, et al. Design and control of compliant tensegrity robots through simulation and hardware validation. J R Soc Interface. 2014;11:20140520. doi: 10.1098/rsif.2014.0520
   PubMed Web of Science ®Google Scholar
• Pitti A, Lungarella M, Kuniyoshi Y. Quantification of emergent behaviors induced by feedback resonance of chaos. Recent Adv Artif Life: Adv Nat Comput. 2005;3(15):199–213. doi: 10.1142/9789812701497_0015
   Google Scholar
• Ingber D, Wang N, Stamenovi D. Tensegrity, cellular biophysics, and the mechanics of living systems. Rep Prog Phys. 2014;046603(77):48–57.
   Google Scholar
• Turvey M, Fonseca S. The medium of haptic perception: a tensegrity hypothesis. J Mot Behav. 2014;46(3):143–187. doi: 10.1080/00222895.2013.798252
   PubMed Web of Science ®Google Scholar
• Levin S. The tensegrity-truss as a model for spine mechanics: biotensegrity. J Mech Med Biol. 2002;2:375–388. doi: 10.1142/S0219519402000472
   Web of Science ®Google Scholar
• Strogatz S. Sync: the emerging science of spontaneous order. New York (NY): Hyperion; 2003.
   Google Scholar
• Bizzi E, Cheung V, d'Avella A, et al. Combining modules for movement. Brain Res Rev. 2008;57(1):125–133. doi: 10.1016/j.brainresrev.2007.08.004
   PubMedGoogle Scholar
• Ting L. Dimensional reduction in sensorimotor systems: a framework for understanding muscle coordination of posture. Prog Brain Res. 2007;165:299–321. doi: 10.1016/S0079-6123(06)65019-X
   PubMed Web of Science ®Google Scholar
• Allen J, Ting L. Why is neuromechanical modeling of balance and locomotion so hard? Chapter 7. J Mot Behav. 2016;7:197–223.
   Google Scholar
• Pitti A, Lungarella M, Kuniyoshi Y. Exploration of natural dynamics through resonance and chaos. Proceedings of the 9th Conference on Intelligent Autonomous Systems; Tokyo, Japan. 2006. p. 558–565
   Google Scholar
• Pitti A, Lungarella R, Kuniyoshi Y. Generating spatiotemporal joint torque patterns from dynamical synchronization of distributed pattern generators. Front Neuro-Robotics. 2009;3(2):2.
   PubMed Web of Science ®Google Scholar
• Pitti A, Niiyama R, Kuniyoshi Y. Creating and modulating rhythms by controlling the physics of the body. Auton Robots. 2010;23(8):317–329. doi: 10.1007/s10514-009-9176-1
   Web of Science ®Google Scholar
• Melnyk A, Henaff P. Bio-inspired plastic controller for a robot arm to shake hand with human. Proceedings of IEEE International Conference on Electronics and Nanotechnology; Kiev, Ukraine. 2016. p. 163–168
   Google Scholar
• Ijspeert A, Nakanishi J, Schaal S. Learning attractor landscapes for learning motor primitives. Cambridge, MA: MIT Press; 2003.
   Google Scholar
• Ijspeert A. Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 2008;21(4):642–653. doi: 10.1016/j.neunet.2008.03.014
   PubMed Web of Science ®Google Scholar
• Frumar A, Zhou Y, Xie Y, et al. Tensegrity structures with 3d compressed components: development, assembly and design. J Int Assoc for Shell and Spatial Struct. 2009;161(2):99–110.
   Google Scholar
• Tinkercad. https://tinkercad.com/things/6ibmnx721ak. 2016.
   Google Scholar
• Bardy B, Oullier O, Lagarde J, et al. On perturbation and pattern co-existence in postural coordination dynamics. J Mot Behav. 2007;39:326–334. doi: 10.3200/JMBR.39.4.326-336
   PubMed Web of Science ®Google Scholar
• Geyer H, Herr H. A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Trans Neural Systems Rehabilitation Eng. 2010;18(3):263–273. doi: 10.1109/TNSRE.2010.2047592
   PubMed Web of Science ®Google Scholar
• Nakanishi J, Morimoto J, Endo G, et al. An empirical exploration of phase resetting for robust biped locomotion with dynamical movement primitives. International Conference on Intelligent Robots and Systems; Sendai, Japan. 2004. p. 919–924.
   Google Scholar
• Hoinville T, Tapia Siles C, Henaff P. Flexible and multistable pattern generation by evolving constrained plastic neurocontrollers. Adapt Behav. 2011;19(3):187–207. doi: 10.1177/1059712311403631
   Web of Science ®Google Scholar
• Alexander M. Walking made simple. Science. 2005;308(5718):58–59.
   PubMed Web of Science ®Google Scholar
• Pfeifer R, Bongard J. How the body shapes the way we think, a new view of intelligence. Cambridge, MA: Bradford Books/The MIT Press; 1999.
   Google Scholar
• Pfeifer R, Gomez G. Morphological computation – connecting brain, body, and environment. In: Sendhoff B, Körner E, Sporns O, et al., editors. Creating brain-like intelligence. Lecture notes in computer science, Vol. 5436. Berlin: Springer. p. 66–83.
   Google Scholar
• Pfeifer R, Pitti A. La révolution de l'intelligence du corps (French). Paris: Manuella Editions; 2012.
   Google Scholar
• Fradkov A. Exploring nonlinearity by feedback. Phys D: Nonlinear Phenom. 1999;128(2):159–168. doi: 10.1016/S0167-2789(98)00322-4
   Web of Science ®Google Scholar
• Fradkov AL. Investigation of physical systems by means of feedback. Autom Remote Control. 1999;60(3):3–22.
   Web of Science ®Google Scholar
• Iida F, Pfeifer R. Self-stabilization and behavioral diversity of embodied adaptive locomotion. In: Iida F, Pfeifer R, Steels L, et al., editors. Embodied artificial intelligence. Lecture notes in computer science, Vol. 3139. Berlin: Springer. p. 119–129.
   Google Scholar
• Ikemoto S, Nishigori Y, Hosoda K. Advantages of flexible musculoskeletal robot structure in sensory acquisition. Artif Life Robot. 2012;17(1):63–69. doi: 10.1007/s10015-012-0017-5
   Google Scholar
• Laumond JP, Benallegue M, Carpentier J, et al. The Yoyo-Man. International Symposium on Robotics Research (ISRR); Sep.; Sestri Levante, Italy; 2015.
   Google Scholar
• Amrollah E, Henaff P. On the role of sensory feedbacks in Rowat-Selverston cpg to improve robot legged locomotion. Front Neurorobot. 2010;4:113. doi: 10.3389/fnbot.2010.00113
   PubMedGoogle Scholar
• Nassour J, Hénaff P, Benouezdou F, et al. Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots. Biol Cybern. 2014;108(3):291–303. doi: 10.1007/s00422-014-0592-8
   PubMed Web of Science ®Google Scholar
• Fujita K, Yonekura S, Nishikawa S, et al. Environmental and structural effects on physical reservoir computing with tensegrity. Proceedings of the 5th IIAE international conference on Intelligent Systems and Image Processing; Hawaii. 2017. p. 1–8.
   Google Scholar
• Berthoz A. The brain's sense of movement. Cambridge (MA): Harvard University Press; 2000.
   Google Scholar
• Falotico E, Cauli N, Kryczka P, et al. Head stabilization in a humanoid robot: models and implementations. Auton Robots. 2017;41(1):349–365. doi: 10.1007/s10514-016-9583-z
   Web of Science ®Google Scholar