Volume 11, Issue 4 (Journal of Control, V.11, N.4 Winter 2018)
JoC 2018, 11(4): 13-24 |
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Alamiyan harandi F, Derhami V. Feature Extraction from Depth Data using Deep Learning for Supervised Control of a Wheeled Robot . JoC 2018; 11 (4) :13-24
URL: http://joc.kntu.ac.ir/article-1-467-en.html
URL: http://joc.kntu.ac.ir/article-1-467-en.html
1- yazd university
Abstract: (18391 Views)
This paper proposes a framework of Supervised Deep Learning (SDL) for wheeled robot navigation in soft terrains with a focus on wall following and obstacle avoidance tasks. Here, it is supposed the robot is only equipped with a vision system (Kinect camera). The main challenge while using depth images is high dimensionality of images and extracting proper features of them with a purpose of reducing input dimensionality of controller. To do this, the deep learning is utilized in this paper and the appropriate features which are the representation of depth images are acquired. Four architectures are created using this features and the history of steering commands. These architectures are compared in WEBOT simulator. The experiments show that the proposed architecture with four groups of features including: the represented features of depth data, previous represented features, the position of trajectory in color image, and the history of previous steering commands can control the robot in soft terrain with a variety of obstacles as well.
Type of Article: Research paper |
Subject:
Special
Received: 2017/03/29 | Accepted: 2017/08/16 | Published: 2017/11/18
Received: 2017/03/29 | Accepted: 2017/08/16 | Published: 2017/11/18
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