Obstacle Avoidance Algorithms for Autonomous Navigation System in Unstructured Indoor Areas

This work presents different approaches to the obstacle avoidance algorithm for a robot that moves in an unknown, unstructured indoor environment. The first step is the investigation and study of the platform, divided into software and hardware, available at the Mechatronics Laboratory (Laboratorio Interdisciplinare di Meccatronica, LIM) at the Politecnico di Torino, on which it is implemented the navigation algorithm. For what is concerned with the software platform, ROS has been used. The Robot Operating System is an open source framework to manage robots’ operations, tasks, motions. It represents the Operating System mounted on the two Turtlebot (Burger, Waffle), hardware platform, that are available at the LIM. After a brief analysis of the kind of sensor needed and handled by the Turtlebot, the second step is the inspection of the different algorithms that are suitable and relevant for our purpose, goal and environment. Many techniques could be used to implement the navigation that is generally divided into global motion planning and local motion control. Often autonomous mobile robots work in an environment for which prior maps are incomplete or inaccurate. They need the safe trajectory that avoids the collision. The algorithms presented in this thesis are related to the local motion planning; therefore, the robot, using the sensor mounted on it, is capable to avoid the obstacles moving toward the free area. The obstacle avoidance algorithms are closer to our starting conditions, so the robot is in a completely unknown area, and the goal is to travel the robot to the area less occupied by obstacles. Three different algorithms of Obstacle Avoidance are presented in this work, that address a complete autonomous navigation in an unstructured indoor environment. The algorithms gradually become more and more complex, and all are tested on the Turtlebot3 robot (Waffle and Burger), where only LiDAR was used as sensor to identify obstacles. The third algorithm, “Autonomous Navigation”, can be considered the final work, the main advantage is the possibility to perform curved trajectory with an accurate choice of the selected path, combining the angular and the linear velocity (3960 different motion), the LiDAR scans 180° in front of the robot to understand the correct direction. The last improvement is the autonomous creation of the map that has advantages and disadvantaged respect to the one created by Rviz, a tool or Ros. The improvement of this reactive obstacle avoidance method is to successfully drive robots in troublesome areas, where other methods present a high degree of difficulty in navigating. We show experimental results on Turtlebot3 to validate this research, and an argumentation about the advantages and limitations.