A Study on behavioral-based algorithm and pre-defined algorithm applied to a self-navigating vacuum cleaner

The study on Behavioral-Based Algorithm and Pre-Defined Algorithm Applied to a Self-Navigating Vacuum Cleaner is a thesis project inspired by recent developments in automation by applying various algorithms to do different tasks. Finding a suitable algorithm for a vacuum cleaner to navigate and clean an environment autonomously, lead to the completion of this project. This thesis project is a comprehensive application of theories in mechatronics (mechanical, electrical, and software design) that were utilized to come up with a prototype. Different algorithms have varying rates of effectivity depending on the orientation of the room and its obstacles. The objective of this research is to test the effectivity of the two algorithms namely the behavioral-based and the pre-defined algorithm applied to a self-navigating robot. The group constructed a robot fitted with a vacuum cleaner to have a unit that can be tested with the algorithms. The two algorithms are used on the same unit. The algorithms applied on the vacuum cleaner are both tested for effectivity on the floors with and without obstacles to come up with results on which algorithm will perform better. Based from the results of the experiments, the group concluded that the two algorithms applied to the vacuum cleaner have its own advantages which are the following: First, the behavioral-based random algorithm has a higher effectivity to maneuver around within its limitations if there are many obstacles in the environment because the obstacles will drive the robot to steer from all areas of the room. Second, the pre-defined algorithm has a higher effectivity of being able to maneuver around within its limitations in a room with less number of obstacles because the obstacles tend to cut the path of the pre-defined algorithm, thus hindering the robot from completing the path which results to uncovered spaces