  Mobile robotics is a research area that has witnessed incredible advances for
the last decades. Robot navigation is an essential task for mobile robots. Many
methods are proposed for allowing robots to navigate within different
environments. This thesis studies different deep learning-based approaches,
highlighting the advantages and disadvantages of each scheme. In fact, these
approaches are promising that some of them can navigate the robot in unknown
and dynamic environments. In this thesis, one of the deep learning methods
based on convolutional neural network (CNN) is realized by software
implementations. There are different preparation studies to complete this
thesis such as introduction to Linux, robot operating system (ROS), C++,
python, and GAZEBO simulator. Within this work, we modified the drone network
(namely, DroNet) approach to be used in an indoor environment by using a ground
robot in different cases. Indeed, the DroNet approach suffers from the absence
of goal-oriented motion. Therefore, this thesis mainly focuses on tackling this
problem via mapping using simultaneous localization and mapping (SLAM) and path
planning techniques using Dijkstra. Afterward, the combination between the
DroNet ground robot-based, mapping, and path planning leads to a goal-oriented
motion, following the shortest path while avoiding the dynamic obstacle.
Finally, we propose a low-cost approach, for indoor applications such as
restaurants, museums, etc, on the base of using a monocular camera instead of a
laser scanner.
