With the advancement of technology and its cheapness, robotic vehicles have gained a large number of applications. The spread of their use is growing also because they are getting smaller, lighter and easier to build. In this paper we present a simple and effective way to map a road network with the help of a driverless vehicle. Our approach consists of only three parts: vision-segmentation, angle variation and travelled distance. A video camera attached to a Lego® NXT Mindstorm vehicle guides it by image segmentation using Matlab® Image processing toolbox, along a road network, in which is represented by black tape over a white floor. The algorithm makes the vehicle travel all over the road memorizing main coordinates to identify all crossroads by keeping track of the travelled distance and the current angle. The crossroads and road’s end are the nodes of the graph. After several simulations have been performed, the modelling proved to be successful in that small scale approach. Consequently, there are good chances that driverless cars and UAVs also make use of the strategies to map route networks accordingly. The algorithm presented in this paper is useful when there is no localization signal such as GPS, for example, navigation on water, tunnels, inside buildings, among others.Faculty Sponsor: Francisco de Assis Zampirolli
Yano, Henrique and Zampirolli, Francisco
"Generating Road Network Graph with Vision-Based Unmanned Vehicle,"
International Journal of Undergraduate Research and Creative Activities: Vol. 9:
1, Article 2.
Available at: https://digitalcommons.cwu.edu/ijurca/vol9/iss1/2
Article download data priot to October 2023