Indoor Navigation of Unmanned Grounded Vehicle using CNN
Arindam Jain1, Ayush Singh2, Deepanshu Bansal3, Madan Mohan Tripathi4
1Arindam Jain*, Department of Electrical Engineering, Delhi Technological University, New Delhi, India.
2Ayush Singh*, Department of Electrical Engineering, Delhi Technological University, New Delhi, India.
3Deepanshu Bansal*, Department of Electrical Engineering, Delhi Technological University, New Delhi, India.
4Prof. Madan Mohan Tripathi*, Department of Electrical Engineering, Delhi Technological University, New Delhi, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1766-1771 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7972038620/2020©BEIESP | DOI: 10.35940/ijrte.F7972.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a hardware and software architecture for an indoor navigation of unmanned ground vehicles. It discusses the complete process of taking input from the camera to steering the vehicle in a desired direction. Images taken from a single front-facing camera are taken as input. We have prepared our own dataset of the indoor environment in order to generate data for training the network. For training, the images are mapped with steering directions, those are, left, right, forward or reverse. The pre-trained convolutional neural network(CNN) model then predicts the direction to steer in. The model then gives this output direction to the microprocessor, which in turn controls the motors to transverse in that direction. With minimum amount of training data and time taken for training, very accurate results were obtained, both in the simulation as well as actual hardware testing. After training, the model itself learned to stay within the boundary of the corridor and identify any immediate obstruction which might come up. The system operates at a speed of 2 fps. For training as well as making real time predictions, MacBook Air was used.
Keywords: Unmanned Ground Vehicle, Self-Driving Vehicle, Convolutional Neural Network, kernel.
Scope of the Article: Networked-Driven Multicourse Chips.