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.
authors.affiliation: Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Universitá degli Studi di Catania, Viale A. Doria 6, Catania, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Arena
authors.forename: Luigi
authors.initials: L
authors.lastname: Fortuna
authors.forename: Mattia
authors.initials: M
authors.lastname: Frasca
authors.forename: Luca
authors.initials: L
authors.lastname: Patane
mesh_terms: D005246:Feedback / Q000502:physiology; D009043:Motor Activity / Q000502:physiology; D016571:Neural Networks, Computer; D012371:Robotics / Q000295:instrumentation / Q000379:methods*