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M. Bulín : Classification of Terrain based on Proprioception and Tactile Sensing for Multi-legged Walking Robot . Master Thesis, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark, 2016.

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Abstract

Proprioception and tactile sensing in insect-like legged robots is a fast, illumination insensitive and biologically inspired way of ground perception. In this thesis, 14 virtually generated terrains are classified based on the mentioned sensor types for a simulated version of hexapod robot AMOS II. A feedforward neural network framework equipped with a novel network pruning algorithm has been developed for classification. We observe over 92% classification accuracy on deterministic terrain data and 72% on manually noised data. The pruning algorithm removes unimportant synapses (generally more than 90%) from a fully-connected network, while the classification accuracy does not drop significantly. The number of input neurons is reduced by 65%, resulting in the minimal network structure for the classification problem. A theory of using minimal structures for feature selection is proposed. The thesis outcome consists of a minimal neural network capable of terrain classification based on selected features of proprioceptive and tactile sensory signals.

Detail of publication

Title: Classification of Terrain based on Proprioception and Tactile Sensing for Multi-legged Walking Robot
Author: M. Bulín
Language: English
Date of publication: 11 Jun 2016
Year: 2016
Type of publication: Habilitation and dissertation theses
Book title: Master Thesis
Publisher: University of Southern Denmark
Address: Campusvej 55, 5230 Odense, Denmark
/ 2020-05-15 14:57:57 /

BibTeX

@MISC{MBulin_2016_Classificationof,
 author = {M. Bul\'{i}n},
 title = {Classification of Terrain based on Proprioception and Tactile Sensing for Multi-legged Walking Robot },
 year = {2016},
 publisher = {University of Southern Denmark},
 address = {Campusvej 55, 5230 Odense, Denmark},
 booktitle = {Master Thesis},
 url = {http://www.kky.zcu.cz/en/publications/MBulin_2016_Classificationof},
}