Title
Sensory feedback in CNN-based central pattern generators.
Abstract
Central Pattern Generators (CPGs) are a suitable paradigm to solve the problem of locomotion control in walking robots. CPGs are able to generate feed-forward signals to achieve a proper coordination among the robot legs. In literature they are often modelled as networks of coupled nonlinear systems. However the topic of feedback in these systems is rarely addressed. On the other hand feedback is essential for locomotion. In this paper the CPG for a hexapod robot is implemented through Cellular Neural Networks (CNNs). Feedback is included in the CPG controller by exploiting the dynamic properties of the CPG motor-neurons, such as synchronization issue and local bifurcations. These universal paradigms provide the essential issues to include sensory feedback in CPG architectures based on coupled nonlinear systems. Experiments on a dynamic model of a hexapod robot are presented to validate the approach introduced.
Year
DOI
Venue
2003
10.1142/S0129065703001698
Int. J. Neural Syst.
Keywords
Field
DocType
central pattern generator
Control theory,Synchronization,Nonlinear system,Computer science,Control theory,Hexapod,Central pattern generator,Robot,Sensory system,Cellular neural network
Journal
Volume
Issue
ISSN
13
6
0129-0657
Citations 
PageRank 
References 
9
0.89
5
Authors
4
Name
Order
Citations
PageRank
Paolo Arena126147.43
Luigi Fortuna2761128.37
Mattia Frasca331360.35
Luca Patané410417.31