scholarly journals Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Atılım Güneş Baydin
Keyword(s):  
Robot Control ◽  
Central Pattern Generators ◽  
Legged Locomotion ◽  
Bipedal Walking ◽  
Biologically Inspired ◽  
Neurophysiological Studies ◽  
Real Robot ◽  
Pattern Generators ◽  
And Performance ◽  
Walking Mechanism

AbstractCentral pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.

Head Motion Stabilization During Quadruped Robot Locomotion

2011 ◽  
Vol 2 (1) ◽  
pp. 39-62 ◽  
Author(s):  
Miguel Oliveira ◽  
Cristina P. Santos ◽  
Lino Costa ◽  
Ana Rocha ◽  
Manuel Ferreira
Keyword(s):  
Global Optimization ◽  
Head Movement ◽  
Nonlinear Dynamical Systems ◽  
Fitness Function ◽  
Central Pattern Generators ◽  
Quadruped Robot ◽  
Biologically Inspired ◽  
Nonlinear Dynamical ◽  
Pattern Generators ◽  
The One

In this work, the authors propose a combined approach based on a controller architecture that is able to generate locomotion for a quadruped robot and a global optimization algorithm to generate head movement stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs) that are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows for explicitly specified parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. The overall idea is to generate head movement opposed to the one induced by locomotion, such that the head remains stabilized. Thus, in order to achieve this desired head movement, it is necessary to appropriately tune the CPG parameters. Three different global optimization algorithms search for this best set of parameters. In order to evaluate the resulting head movement, a fitness function based on the Euclidean norm is investigated. Moreover, a constraint-handling technique based on tournament selection was implemented.

A General Rhythmic Pattern Generation Architecture for Legged Locomotion

2011 ◽  
pp. 202-230
Author(s):  
Zhijun Yang ◽  
Felipe M.G. França
Keyword(s):  
Critical Role ◽  
Central Pattern Generators ◽  
Legged Locomotion ◽  
Building Blocks ◽  
Pattern Generation ◽  
Rhythmic Pattern ◽  
Motor Information ◽  
Pattern Generators ◽  
Life Phenomena ◽  
Almost All

As an engine of almost all life phenomena, the motor information generated by the central nervous system (CNS) plays a critical role in the activities of all animals. After a brief review of some recent research results on locomotor central pattern generators (CPG), which is a concrete branch of studies on the CNS generating rhythmic patterns, this chapter presents a novel, macroscopic and model-independent approach to the retrieval of different patterns of coupled neural oscillations observed in biological CPGs during the control of legged locomotion. Based on scheduling by multiple edge reversal (SMER), a simple and discrete distributed synchroniser, various types of oscillatory building blocks (OBB) can be reconfigured for the production of complicated rhythmic patterns and a methodology is provided for the construction of a target artificial CPG architecture behaving as a SMER-like asymmetric Hopfield neural networks.

scholarly journals An optimality principle for locomotor central pattern generators

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hansol X. Ryu ◽  
Arthur D. Kuo
Keyword(s):  
Internal Model ◽  
Sensory Feedback ◽  
Motor Pattern ◽  
Central Pattern Generators ◽  
Optimal Estimation ◽  
Legged Locomotion ◽  
Energetic Cost ◽  
Fictive Locomotion ◽  
Cost Of Transport ◽  
Pattern Generators

AbstractTwo types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re-interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with neural circuitry compatible with classic CPG models, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback.

scholarly journals Adaptive and Energy-Efficient Optimal Control in CPGs Through Tegotae-Based Feedback

2021 ◽  
Vol 8 ◽  
Author(s):  
Riccardo Zamboni ◽  
Dai Owaki ◽  
Mitsuhiro Hayashibe
Keyword(s):  
Energy Efficiency ◽  
Force Feedback ◽  
Central Pattern Generators ◽  
The State ◽  
Proprioceptive Information ◽  
Robotic Control ◽  
Periodic Patterns ◽  
Biologically Inspired ◽  
Optimal Energy ◽  
Pattern Generators

To obtain biologically inspired robotic control, the architecture of central pattern generators (CPGs) has been extensively adopted to generate periodic patterns for locomotor control. This is attributed to the interesting properties of nonlinear oscillators. Although sensory feedback in CPGs is not necessary for the generation of patterns, it plays a central role in guaranteeing adaptivity to environmental conditions. Nonetheless, its inclusion significantly modifies the dynamics of the CPG architecture, which often leads to bifurcations. For instance, the force feedback can be exploited to derive information regarding the state of the system. In particular, the Tegotae approach can be adopted by coupling proprioceptive information with the state of the oscillation itself in the CPG model. This paper discusses this policy with respect to other types of feedback; it provides higher adaptivity and an optimal energy efficiency for reflex-like actuation. We believe this is the first attempt to analyse the optimal energy efficiency along with the adaptivity of the Tegotae approach.

Introducing a nonlinear coupling for central pattern generator: Improvement on robustness by expanding basin of attraction and performance by decreasing the transient time

2020 ◽  
Vol 26 (7-8) ◽  
pp. 377-386
Author(s):  
Alireza Bahramian ◽  
Ali Nouri ◽  
Farzad Towhidkhah ◽  
Hamed Azarnoush ◽  
Sajad Jafari
Keyword(s):  
Phase Difference ◽  
Basin Of Attraction ◽  
Central Pattern Generators ◽  
Nonlinear Oscillators ◽  
Nonlinear Coupling ◽  
Transient Time ◽  
Coupled Nonlinear Oscillators ◽  
Pattern Generators ◽  
And Performance ◽  
The Basin Of Attraction

Different types of models have been introduced for central pattern generators mostly based on coupled nonlinear oscillators. One of the most important responsibilities of a central pattern generators is to make an acceptable phase for each limb to make a stable motion. In nonlinear oscillators, the phase difference is made by means of commensurate coupling between them. Linear coupling between oscillators has been widely used in the literature. It is used to make a suitable phase difference between oscillators appropriate to the kind of motion (e.g. walking and running). In this research, it is shown that there are some coexisting attractors in the same coupling, in which phases between oscillators are not ideal to generate rhythms of motion. Consequently, there will be some undesired oscillator states, in which their functionality is significantly decreased. In this article, a novel nonlinear coupling is introduced as a solution for this problem to have a more robust central pattern generators by tackling inappropriate attractors and expanding the basin of attraction of the suitable attractor. In addition, this nonlinear coupling for central pattern generators cues arrived their steady state 2.8 times faster than central pattern generators with linear ones.

scholarly journals An optimality principle for locomotor central pattern generators

2019 ◽  
Author(s):  
Hansol X. Ryu ◽  
Arthur D. Kuo
Keyword(s):  
Internal Model ◽  
Sensory Feedback ◽  
Motor Pattern ◽  
Central Pattern Generators ◽  
Optimal Estimation ◽  
Legged Locomotion ◽  
Energetic Cost ◽  
Fictive Locomotion ◽  
Cost Of Transport ◽  
Pattern Generators

AbstractTwo types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re- interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with classic neural half-center circuitry, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback.New and NoteworthySensory feedback modulates the central pattern generator (CPG) rhythm in locomotion, but there lacks an explanation for how much feedback is appropriate. We propose destabilizing noise as a determinant, where an uncertain environment demands more feedback, but noisy sensors demand less. We reinterpret the CPG as an internal model for predicting body state despite noise. Optimizing its feedback yields robust and economical gait in a walking model, and explains the advantages of feedback-driven CPG control.

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