Biped locomotion control through a biomimetic CPG-based controller

Abstract

Modern concepts of motor learning favour intensivetrainingdirectedtotheneuralnetworksstimulation and reorganization within the spinal cord, the central pattern generator, by taking advantage of the neural plasticity. In the present work, a biomimetic controller using a system of adaptive oscillators is proposed to understand the neuronal principles underlying the human locomotion. A framework for neural control is presented, enabling the following contributions: a) robustness to external perturbations; b) flexibility to variations in the environmental constraints; and c) incorporation of volitional mechanisms for self-adjustment of gait dynamics. Phase modulation of adaptive oscillators and postural balance control are proposed as main strategies for stable locomotion. Simulations of the locomotion model with a biped robot in closed-loop control are presented to validate the implemented neuronal principles. Specifically, the proposed system for online modulation of previous learnt gait patterns was verified in terrains with different slopes. The proposed phase modulation method and postural balanced control enabled robustness enhancement considering a broader range of slope angles than recent studies. Furthermore, the system was also verified for tilted ground including different slopes in the same experiment and uneven terrain with obstacles. Adaptive Frequency Oscillators, under Dynamic Hebbian Learning Adaptation mechanism, are proposed to build a hierarchical control architecture with spinal and supra spinal centers with multiple rhythm-generating neural networks that drive the legs of a biped model. The proposed neural oscillators are based on frequency adaptation and can be entrained by sensory feedback to learn specific patterns. The proposed biomimetic controller intrinsically generates patterns of rhythmic activity that can be induced to sustain CPG function by specific training. This method provides versatile control, paving the way for the design of experimental motor control studies, optimal rehabilitation procedures and robot-assisted therapeutic outcomes.