This project aims to enhance the efficiency of Roomba robots in real-world settings by comparing the performance of different algorithms. The research uses pre-existing pathfinding algorithms for iRobot Create 3 to implement a coverage measuring system. A comparative analysis will be conducted measuring and evaluating key factors such as coverage, time consumption and overall efficiency of each algorithm.

A primary focus for this project is simultaneous localization and mapping (SLAM), a technique that enables a robot to create and update a map of an unknown environment while simultaneously keeping track of its position. We will utilize LiDAR technology installed on iRobot Create 3 and a Raspberry Pi 4 running ROS2 to implement a system that creates a map of the room covered and records the robot’s path. These maps will be generated using software designed for mapping and visualization (such as Nav2 and RViz).

The generated maps are then analyzed, and the data recorded is compared to identify algorithms that provide more comprehensive coverage. We anticipate that this research will contribute to creating a more systematic approach to analyzing pathfinding strategies reaching beyond the algorithms used in the comparative analysis in this study. The results of this project will potentially be used in more informed decision-making in the selection of algorithms for autonomous consumer-grade vacuuming robots used in diverse environments.