MATEC Web Conf.
Volume 161, 201813th International Scientific-Technical Conference on Electromechanics and Robotics “Zavalishin’s Readings” - 2018
|Number of page(s)||6|
|Section||Robotics and Automation|
|Published online||18 April 2018|
Reinforcement learning and convolutional neural network system for firefighting rescue robot
Electrical Engineering, National Taipei University, 23741 Sanxia New Taipei City, Taiwan
* Corresponding author: firstname.lastname@example.org
In this paper, we combine the machine learning and neural network to build some modules for the fire rescue robot application. In our research, we build the robot legs module with Q-learning. We also finish the face detection with color sensors and infrared sensors. It is usual that image fusion is done when we want to use two kinds of sensors. Kalman filter is chosen to meet our requirement. After we finish some indispensable steps, we use sliding windows to choose our region of interest to make the system’s calculation lower. The least step is convolutional neural network. We design a seven layers neural network to find the face feature and distinguish it or not.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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