Authors:
Eniko T. Enikov
and
Phillip Vidinski
Affiliation:
University of Arizona, United States
Keyword(s):
Spherical Parallel Mechanism, Artificial Neural Network, Body Schema,Cognitive Robotics.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Cognitive Robotics
;
Human-Machine Interfaces
;
Human-Robots Interfaces
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
Abstract:
Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brains, allowing
efficient representation of non-linear mapping between the body configuration space, i.e. its generalized coordinates
and the resulting sensory outputs. This paper describes the development of closed-loop control of
spherical parallel mechanism based on self-learning body schemas. More specifically, we demonstrate how
a complex parallel spherical manipulator in contact with a surface of irregular geometry can be driven to a
configuration of balanced contact forces, i.e. aligned with respect to the irregular surface. The approach uses
a pseudo-potential functions and a gradient-based maximum seeking algorithm to drive the manipulator to
the desired position. It is demonstrated that a neural-gas type neural network, trained through Hebbian-type
learning algorithm can learn a mapping between the manipulator’s rotary degrees of freedom and the output
contact forces. Numerical and ex
perimental results are presented illustrating the performance of the control
scheme. A motivating application of the proposed manipulator and its control algorithm is a hand-held eye
tonometer based on tactile force measurements. The resulting controller has been shown to achieve 10 mN of
force errors which are adequate for tactile tonometers.
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