Reinforcement learning and convolutional neural network system for firefighting rescue robot

Tien Kun Yu, Yang Ming Chieh, Hooman Samani

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number3028
JournalMATEC Web of Conferences
Publication statusPublished - 18 Apr 2018
Event13th International Conference on Electromechanics and Robotics "Zavalishin's Readings", ER(ZR) 2018 - St. Petersburg, Russian Federation
Duration: 18 Apr 201821 Apr 2018


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