Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approach

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.