Synchronization of Non-linear Oscillators for Neurobiologically Inspired Control on a Bionic Parallel Waist of Legged Robot

Abstract

Synchronization of coupled non-linear oscillators inspired by a central pattern generator (CPG) can control the bionic robot and promote the coordination and diversity of locomotion. However, for a robot with a strong mutual coupled structure, such neurobiological control is still missing. In this contribution, we present a σ-Hopf harmonic oscillator with decoupled parameters to expand the solution space of the locomotion of the robot. Unlike the synchronization of original Hopf oscillators, which has been fully discussed, the asymmetric factor of σ-Hopf oscillator causes a deformation in oscillation waveform. Using the non-linear synchronization theory, we construct the transition state model of the synchronization process to analyze the asymmetrical distortion, period change and duty ratio inconsistency. Then a variable coupling strength is introduced to eliminate the waveform deformation and maintain the fast convergence rate. Finally, the approach is used for the locomotion control of a bionic parallel waist of legged robot, which is a highly coupled system. The effectiveness of the approach in both independent and synthesis behavior of four typical motion patterns are validated. The result proves the importance of controllability of the oscillation waveform and the instantaneous state of the synchronization, which benefits the transition and transformation of the locomotion and makes the coupling motion more flexible.

RETRACTED: Mechanisms Contributing to the Generation of Mayer Waves

“…The model developed by Myers et al (2001) accurately predicts behavior of Mayer waves, indicating a simple bivariate linear autoregressive model may reasonably predict nonlinear behavior despite the inherent limitations inherent to a linear model and fruitful hypotheses subject to empirical interrogation. Accordingly, the mathematical relationships representing emergent genesis and dynamic properties of Mayer waves may be expressed in the form of nonlinear differential equations (Cavalcanti and Belardinelli, 1996;Czosnyka et al, 2018), difference equations (Cushing, 2019), non-linear oscillators (Zhu et al, 2019), regularization theory (Chen and Haykin, 2002), non-linear fluid dynamics (Olufsen et al, 2012), non-linear time series analysis (de la Cruz et al, 2019), and systems identification, in order to investigate interactions amongst the oscillations (Plakias and Boutalis, 2019). We present a conceptual neurobiological framework through which to apprehend the development of a centrogenic model explaining initiation, maintenance, and propagation of Mayer waves, multivariately modulated by baroreflex mechanisms and retro-arterially propagated and neurointerstitially mechanotransductively conveyed oscillations of arteriolar diameter (Seydnejad and Kitney, 2001).…”

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

“…The learning system mainly implements the decision-making and motion planning of the intelligent basketball robot through environmental map construction and path planning. Machine learning includes supervised learning, unsupervised learning, and reinforcement learning (Zhu et al, 2019 ; Zhang et al, 2020 ). Among which, reinforcement learning emphasizes how to evaluate selection actions based on the environment to maximize the expected benefits.…”

Generic Mechanism for Waveform Regulation and Synchronization of Oscillators: An Application for Robot Behavior Diversity Generation

“…It is also worth noting that the oscillator synchronization process represents the interaction among the oscillators in the CPGs. The difference between the instantaneous position and the instantaneous speed of the signal will be amplified during the synchronization process [39].…”