Convolutional Neural Networks Towards Arduino Navigation of Indoor Environments
This work provides insights for hobbyists and educators interested in building low-cost autonomous robots for indoor navigation.
This paper explores several methods, including Canny Edge Detection, Supervised Floor Detection, and Imitation Learning, for enabling a low-budget demo car to navigate indoor environments. It contrasts their effectiveness and details the successes and failures of each approach.
In this paper we propose a number of tested ways in which a low-budget demo car could be made to navigate an indoor environment. Canny Edge Detection, Supervised Floor Detection and Imitation Learning were used separately and are contrasted in their effectiveness. The equipment used in this paper approximated an autonomous robot configured to work with a mobile device for image processing. This paper does not provide definitive solutions and simply illustrates the approaches taken to successfully achieve autonomous navigation of indoor environments. The successes and failures of all approaches were recorded and elaborated on to give the reader an insight into the construction of such an autonomous robot.