Literature DB >> 18555958

Central pattern generators for locomotion control in animals and robots: a review.

Auke Jan Ijspeert1.   

Abstract

The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.

Mesh:

Year:  2008        PMID: 18555958     DOI: 10.1016/j.neunet.2008.03.014

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  153 in total

1.  Integration of intrinsic muscle properties, feed-forward and feedback signals for generating and stabilizing hopping.

Authors:  D F B Haeufle; S Grimmer; K-T Kalveram; A Seyfarth
Journal:  J R Soc Interface       Date:  2012-01-04       Impact factor: 4.118

2.  A mathematical modeling study of inter-segmental coordination during stick insect walking.

Authors:  Silvia Daun-Gruhn
Journal:  J Comput Neurosci       Date:  2010-06-22       Impact factor: 1.621

3.  Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter.

Authors:  Gerasimos Rigatos
Journal:  Cogn Neurodyn       Date:  2014-07-03       Impact factor: 5.082

4.  Mathematical Frameworks for Oscillatory Network Dynamics in Neuroscience.

Authors:  Peter Ashwin; Stephen Coombes; Rachel Nicks
Journal:  J Math Neurosci       Date:  2016-01-06       Impact factor: 1.300

5.  Emergence of the advancing neuromechanical phase in a resistive force dominated medium.

Authors:  Yang Ding; Sarah S Sharpe; Kurt Wiesenfeld; Daniel I Goldman
Journal:  Proc Natl Acad Sci U S A       Date:  2013-06-03       Impact factor: 11.205

6.  Swim pacemaker response to bath applied neurotransmitters in the cubozoan Tripedalia cystophora.

Authors:  Jan Bielecki; Gösta Nachman; Anders Garm
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2013-07-28       Impact factor: 1.836

7.  Optimal control of a hybrid rhythmic-discrete task: the bouncing ball revisited.

Authors:  Renaud Ronsse; Kunlin Wei; Dagmar Sternad
Journal:  J Neurophysiol       Date:  2010-02-03       Impact factor: 2.714

8.  Influence of simulated neuromuscular noise on the dynamic stability and fall risk of a 3D dynamic walking model.

Authors:  Paulien E Roos; Jonathan B Dingwell
Journal:  J Biomech       Date:  2011-03-26       Impact factor: 2.712

9.  Neural oscillators triggered by loading and hip orientation can generate activation patterns at the ankle during walking in humans.

Authors:  Sook-Yee Chong; Heiko Wagner; Arne Wulf
Journal:  Med Biol Eng Comput       Date:  2012-07-29       Impact factor: 2.602

Review 10.  Stiffness as a Risk Factor for Achilles Tendon Injury in Running Athletes.

Authors:  Anna V Lorimer; Patria A Hume
Journal:  Sports Med       Date:  2016-12       Impact factor: 11.136

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.