Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots

Abstract

In this paper, we present an extended mathematical model of the central pattern generator (CPG) in the spinal cord. The proposed CPG model is used as the underlying low-level controller of a humanoid robot to generate various walking patterns. Such biological mechanisms have been demonstrated to be robust in locomotion of animal. Our model is supported by two neurophysiological studies. The first study identified a neural circuitry consisting of a two-layered CPG, in which pattern formation and rhythm generation are produced at different levels. The second study focused on a specific neural model that can generate different patterns, including oscillation. This neural model was employed in the pattern generation layer of our CPG, which enables it to produce different motion patterns-rhythmic as well as non-rhythmic motions. Due to the pattern-formation layer, the CPG is able to produce behaviors related to the dominating rhythm (extension/flexion) and rhythm deletion without rhythm resetting. The proposed multi-layered multi-pattern CPG model (MLMP-CPG) has been deployed in a 3D humanoid robot (NAO) while it performs locomotion tasks. The effectiveness of our model is demonstrated in simulations and through experimental results.

Methods Synthesis of Central Pattern Generator Inspired Biped Walking Control

“…The current study mainly stays in the simulation stage or just rhythmic movement control for some joints of a biped robot. Currently, the mainly used methods are joint space control methods [7,9,10,[12][13][14]. In this method, one CPG unit is assigned to one DoF, and the distributed CPG network can generate complex coordinated multidimensional signals used as force or torque control to realize the coordinated motion.…”

“…Taga's seminal contribution on neuromechanical simulations introduces CPGs to biped robot walking control [7,8]. Nassour et al [9] presented a CPG controller for the real-time balance of a simulated humanoid robot. The behavior of the robot emerges from dynamic interactions between the neural networks, the robot, and the simulated world.…”

Learning diverse motor patterns with a single multi-layered multi-pattern CPG for a humanoid robot

“…However, prior to this, Nassour et al [1] employed MLMP CPG on adaptive locomotion of humanoid robots, which was a lower body task. There were certain parameters that had to be optimized or properly chosen, for each of the two tasks -reaching and writing, in order to achieve the desired behavior.…”

“…Earlier work had presented a Multi-Layered Multi-Pattern (MLMP) CPG model in successfully performing lower body tasks of humanoid robot, like locomotion and reaction for physical disturbances [1]. One motivation of our work was to put forward a generalized model that can control robots in both upper and lower body tasks based on one unique pattern generator.…”

Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control

“…Theoretical models of CPGs range from detailed biophysical models (Hellgren and Grillner, 1992) to pure mathematical oscillators (Matsuoka, 1985). In general, CPGs can be described as nonlinear oscillators which have been used in numerous applications for different variants of robotic control problems (Nakamura et al, 2007; Ijspeert, 2008; Pinto et al, 2012; Nassour et al, 2014; Santos et al, 2017). For instance, compared to purely reflexive control schemes (Foth and Bässler, 1985; Cruse et al, 1995), oscillator-controlled robots enable more stable and robust locomotion (Kimura et al, 2001; Righetti and Ijspeert, 2008).…”