Examining the Effectiveness and Application of Weighted Random Algorithms: A Research Study on Computational Diversity and Performance Improvement

Presenter

David Restrepo

Group Members

David C. Osei-Amoah

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 A21, Campus Center Auditorium, Row 2 (A21-A40) [Poster Location Map]

Abstract

Weighted random algorithms work by assigning numeric values to different objects. Objects with higher values are more likely to be chosen at random. Through our research, we have not found much in terms of this kind of algorithm being implemented to improve on the already random pathing algorithm of vacuuming robots. This algorithm notoriously takes a very long time to completely clean a room and there may be areas that are left untouched by the robot’s pathing algorithm. Our objective is to implement a weighted random algorithm on a robot and compare the results to the default random algorithm and determine which algorithm is more efficient, based on time and overall coverage. Using SLAM (Simultaneous Localization and Mapping), a concept of constructing or updating a map of an unknown environment while simultaneously keeping track of its location, the robot will map out the entire open space and then divide said space into individual segments. Each segment will have the same initial weight and the weight of the segments will increase or decrease depending on if the segment has been traversed or not. We expect that this will help to solve the issues that plague these vacuuming robots by offering a more efficient algorithm for completely covering an open space in a shorter amount of time.

Keywords

space filling algorithm, weighted random algorithm, robotics

Research Area

Computer Science

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