Study and implementation of LiDAR-based SLAM algorithm and map-based autonomous navigation for a telepresence robot to be used as a chaperon for smart laboratory requirements

Abstract

The field of smart autonomous systems has shown rapid growth in the past years, which has led to the development of robots for human assistance. Here, the main focus of the autonomous telepresence robot is to assist as a chaperon in a smart laboratory setup. The goal is to add functionalities to a telepresence robot to help the people in the lab with various everyday tasks like controlling the intelligent lights, doors and windows, and thermostat adjustments. The robot should follow the standard protocols inspired by humans, specifically perception, thinking, and reaction. Simultaneous Localization and Mapping (SLAM) is a significant problem, where the objective is to make the robot cognizant of its position in the surroundings while building the map of the environment. There are many answers to the SLAM problem developed in the past two decades, and in this Master thesis, we study Hector SLAM and implement a solution for map-based autonomous navigation of the robot to enable it to reach a goal position. We also compare the algorithm’s performance by testing how accurately the robot navigates using the map generated by the algorithm in the actual environment of the laboratory. We have a Telepresence Robot from the Ohmni labs, modified to integrate a ydlidar and a Raspberry pi for the test bench. The robot performs navigation using the hector SLAM algorithm and metrics for localization and mapping. The robot explores the indoor environment and at the same time generates a map of it. It uses the map to localize itself in the environment and reach a goal point given to it by a custom script. The algorithm produces a relatively accurate map of the environment while measuring the robot location consistently for various speeds. The robot localizes itself accurately in two different environments and navigates to given goal coordinates. The results from the thesis are accurate and we get essential environmental perceptions (map of the surrounding, ability to localize the robot, optimal driving velocity of the robot )that can make future development work on the Ohmni robot easier. The thesis opens up the possibility of implementing motion and path planning use cases on the robot as the navigation stack of the robot is configured correctly in this work.