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Open AccessArticle

FumeBot: A Deep Convolutional Neural Network Controlled Robot

School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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Robotics 2019, 8(3), 62; https://doi.org/10.3390/robotics8030062
Received: 19 June 2019 / Revised: 19 July 2019 / Accepted: 25 July 2019 / Published: 29 July 2019
(This article belongs to the Section Robotics & Automation)
This paper describes the development of a convolutional neural network for the control of a home monitoring robot (FumeBot). The robot is fitted with a Raspberry Pi for on board control and a Raspberry Pi camera is used as the data feed for the neural network. A wireless connection between the robot and a graphical user interface running on a laptop allows for the diagnostics and development of the neural network. The neural network, running on the laptop, was trained using a supervised training method. The robot was put through a series of obstacle courses to test its robustness, with the tests demonstrating that the controller has learned to navigate the obstacles to a reasonable level. The main problem identified in this work was that the neural controller did not have memory of past actions it took and a past state of the world resulting in obstacle collisions. Options to rectify this issue are suggested. View Full-Text
Keywords: artificial intelligent robot; convolutional neural network; environmental sensing; autonomous mobile robot; home monitoring artificial intelligent robot; convolutional neural network; environmental sensing; autonomous mobile robot; home monitoring
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Thomas, A.; Hedley, J. FumeBot: A Deep Convolutional Neural Network Controlled Robot. Robotics 2019, 8, 62.

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