Implementation and Optimization of the Zigzag Scanline Algorithm for Improved Navigation of Roomba Robot

Presenter

Liam James Maher

Group Members

Imtiaz Rahman

Campus

Quinsigamond Community College

Sponsor

Hao Loi, Department of Computer Science, Quinsigamond Community College

Schedule

Session 2, 11:30 AM - 12:15 PM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A2, Campus Center Auditorium, Row 1 (A1-A20) [Poster Location Map]

Abstract

Our research aims to refine the efficiency of Roomba robots by integrating LiDAR technology, real-time data mapping, and a state-of-the-art navigation algorithm. We will implement the zigzag scanline algorithm in a Roomba robot and compare its efficacy with other navigation algorithms. The navigational tools implemented are crucial for smooth traversal and obstacle avoidance.

To enable effective navigation, our robot will utilize a LiDAR sensor to scan indoor environments, such as houses, warehouses, or offices, and generate 2D maps. These maps, along with the assistance of a Raspberry Pi 4 running ROS2, will facilitate the robot's navigation tasks. Accuracy is crucial, and we will use thorough metrics and analysis to refine mapping and pathfinding processes.

Additionally, we aim to enhance the algorithm's efficiency by measuring metrics such as the time taken to clean a room, the coverage of the cleaning path, and the effectiveness of obstacle avoidance. We will also analyze its performance in different room layouts and sizes to ensure robustness. By optimizing the zigzag navigation algorithm and integrating it with LiDAR technology and real-time data mapping, we aim to significantly enhance the overall efficiency and performance of Roomba robots in real-world settings.

Keywords

Scanline Zigzag Algorithm , Algorithm, ROS, Navigation, Robot

Research Area

Algorithms, Combinatorics, Graph Theory, and Game Theory

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