Authors:
André Frank Krause
;
Thierry Hoinville
;
Nalin Harischandra
and
Volker Dürr
Affiliation:
Bielefeld University, Germany
Keyword(s):
Tactile Sensor, Contour Tracing, Shape Recognition, Artificial Neural Network.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Autonomous Systems
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Information Systems Analysis and Specification
;
Methodologies and Methods
;
Methodologies and Technologies
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Operational Research
;
Pattern Recognition
;
Physiological Computing Systems
;
Robot and Multi-Robot Systems
;
Sensor Networks
;
Signal Processing
;
Simulation
;
Soft Computing
;
Theory and Methods
Abstract:
We propose Contour-Net as a bio-inspired model for rhythmic movement control of a pair of insectoid feelers,
able to successively sample the contour of arbitrarily shaped objects. Initial object contact initiates a smooth
transition from a large-amplitude, low-frequency searching behaviour to a local, small-amplitude and high frequency
sampling behaviour. Both behavioural states are defined by the parameters of a Hopf Oscillator.
Subsequent contact signals trigger a 180º phase-forwarding of the oscillator, resulting in repeated sampling of
the object. The local sampling behaviour effectively serves as a contour-tracing method with high robustness,
even for complicated shapes. Collected contour data points can be directly fed into an artificial neural network
to classify the shape of an object. Given a sufficiently large training dataset, tactile shape recognition can be
achieved in a position-, orientation- and size-invariant manner. Only minimal pre-processing (normalisation
)
of contour data points is required.
(More)