Implementation and Analysis of Advanced Navigation Algorithms for Robotic Vacuum Cleaners Using Webots Simulator

Robotic vacuum cleaners have increasingly become a staple in modern households, providing an automated and efficient means of maintaining cleanliness with minimal human oversight.The core of these robots’ functionality lies in their navigation and obstacle avoidance systems, which are driven by sophisticated algorithms that enable them to move efficiently within a space, navigate around obstacles, and optimize their cleaning coverage.This paper conducts a comprehensive analysis of several advanced navigation algorithms implemented on a robotic vacuum cleaner model within the webots simulation environment. The algorithms explored in this study include Breadth-First Search (BFS) Cleaning, Simultaneous Localization and Mapping (SLAM) combined with a Zigzag Pattern, Boustrophedon Motion, Wavefront Planning with Potential Field Method, A* Path Planning, Probabilistic Harmonic Functions (PHF), Rapidly-Exploring Random Trees (RRT), and Random Walk. The performance of these algorithms is rigorously evaluated in terms of total area cleaned, path efficiency, and obstacle avoidance, using a fixed period of 40 minutes as the benchmark. The findings of this study reveal essential trade-offs between algorithm complexity, computational overhead, and overall cleaning effectiveness. These insights provide a roadmap for future advancements in robotic vacuum cleaner technology, potentially paving the way for more intelligent, efficient, and adaptive cleaning systems.