Synergistic control of a multi-segments vertebral column robot based on tensegrity for postural balance
Résumé
We present a neuronal architecture to control a compliant robotic model of the human vertebral column for postural balance. The robotic structure is designed using the principle of tensegrity that ensures to be lightweight, auto-replicative with multi-degrees of freedom, flexible and also robust to perturbations. We model the central pattern generators of the spinal cords with a network of nonlinear Kuramoto oscillators coupled internally and externally to the structure and error-driven by a proportional derivative (PD) controller using an accelerometer for feedback. This coupling between the two controllers is original and we show it serves to generate controlled rhythmical patterns. We observe for certain coupling parameters some intervals of synchronization and of resonance of the neural units to the tensile structure to permit smooth control and balance. We show that the top-down PD control of the oscillators flexibly absorbs external shocks proportionally to the perturbation and converges to steady state behaviors. We discuss then about our neural architecture to model motor synergies for compliance control and also about tensegrity structures for soft robotics. The 3D printed model is provided as well as a movie at the address
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