The Central Pattern Generator: a paradigm for artificial locomotion

Arena, Paolo

doi:10.1007/s005000000051

Cited by 31 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biological CPGs do not work in isolation; they depend on the information interaction with other parts of the central nervous system [32]; even, external afferent inputs are used to shape their outputs [33]. Based on the aforementioned, the next step of SCPG-based locomotion systems could be their integration in navigation systems to endow them with sensors and can build more robust and plausible bioinspired algorithms.…”

Section: Discussionmentioning

confidence: 99%

Spiking Central Pattern Generators through Reverse Engineering of Locomotion Patterns

Espinal¹,

Sotelo-Figueroa²,

Estrada-García³

et al. 2018

Cognitive and Computational Neuroscience - Principles, Algorithms and Applications

View full text Add to dashboard Cite

In robotics, there have been proposed methods for locomotion of nonwheeled robots based on artificial neural networks; those built with plausible neurons are called spiking central pattern generators (SCPGs). In this chapter, we present a generalization of reported deterministic and stochastic reverse engineering methods for automatically designing SCPG for legged robots locomotion systems; such methods create a spiking neural network capable of endogenously and periodically replicating one or several rhythmic signal sets, when a spiking neuron model and one or more locomotion gaits are given as inputs. Designed SCPGs have been implemented in different robotic controllers for a variety of robotic platforms. Finally, some aspects to improve and/or complement these SCPG-based locomotion systems are pointed out.

show abstract

Section: Discussionmentioning

confidence: 99%

Spiking Central Pattern Generators through Reverse Engineering of Locomotion Patterns

Espinal¹,

Sotelo-Figueroa²,

Estrada-García³

et al. 2018

Cognitive and Computational Neuroscience - Principles, Algorithms and Applications

View full text Add to dashboard Cite

show abstract

“…Biologically, CPG is a distributed neural network, located in the spinal cord of vertebrates, able to generate complex high dimensional signals for the control of coordinated periodic movements [5]. CPG neuron circuits can be modelled as connectionist models [6], vector maps [7], and mostly oscillators [8,9]. With a special structure, the coupled oscillators can produce rhythmic signals regardless of the initial value, i.e., the system is stable, making CPG-based locomotion controllers suitable for legged robots.…”

Section: Introductionmentioning

confidence: 99%

CPGs with Continuous Adjustment of Phase Difference for Locomotion Control

Teng

Chen

et al. 2013

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

The central pattern generator (CPG) has been found to be a real, existing neuron controller for the locomotion control of animals and it has been used on bio-inspired robots widely in recent years. However, research on the adaptability of CPG-based locomotion control methods is still a challenge. In particular, the performance of the CPG method on quadruped robots is not good enough in some situations compared with the traditional force control methods. In this article, we adopt a CPG method in which phase difference between oscillators can be arbitrarily adjusted, and we try to improve the CPG's applications in quadruped robots in some aspects. One aspect is static walk gait locomotion, in which we try to add a transition state in the CPG network to enhance the static balance of the robot. Another aspect is gait transition. Compared with the traditional abrupt gait transition, we try to realize a continuous gait transition between walk gait and trot gait to decrease the fluctuations of the robot. The improved CPG method is tested on a quadruped model and it shows positive results with regard to the improvement of static walk gait and gait transitions.

show abstract

“…Kimura et al realized quadruped dynamic walking on irregular terrain in outdoor environment using a CPG model with a sensory feedback [3], [4]. The CNN-CPG model constructed with electrical circuit was proposed by Arena et al It was used on a hexapod robot which was able to change gaits based on motor map [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A cubic CPG model for snake-like robot to adapt to environment

Tang

et al. 2010

The 2010 IEEE International Conference on Information and Automation

View full text Add to dashboard Cite

-In this paper, we propose a new CPG model for the snake-like robot being able to adapt to different environments. This model can produce typical serpentine, sidewinding and traveling wave locomotion gaits and switch between them automatically based on a simple input signal. The stability of this model is verified and the parameter restrictions are determined. Then, we investigate the relationship between the properties of CPG output and the parameters. Finally, the adaptive characters of this model is discussed. We found that there is an amazing similarity between the locomotion patterns produced by the CPG model and those of the real snake. The contribution of this work makes the snake-like robot be more adaptive to complex environments.

show abstract

The Central Pattern Generator: a paradigm for artificial locomotion

Cited by 31 publications

References 31 publications

Spiking Central Pattern Generators through Reverse Engineering of Locomotion Patterns

Spiking Central Pattern Generators through Reverse Engineering of Locomotion Patterns

CPGs with Continuous Adjustment of Phase Difference for Locomotion Control

A cubic CPG model for snake-like robot to adapt to environment

Contact Info

Product

Resources

About