Robotic Application of the Ant Colony Optimization Algorithm
Faculty Sponsor
Sami Khorbotly
College
Engineering
Discipline(s)
Electrical and Computer Engineering
Presentation Type
Poster Presentation
Symposium Date
Spring 2015
Abstract
The Ant Colony Optimization (ACO) Algorithm is generally used to find the shortest path between an origin and a destination within certain constraints. As the name suggests, the algorithm is bio-inspired by the behavioral model of ants. When searching for food, each ant starts moving in a random direction while secreting pheromones (an invisible chemical) on their traveled paths. Among all ants, only those that are successful return to their home; amongst those that are successful, the ones that travel shorter paths will return more frequently than others, leaving behind higher concentrations of pheromones. An ant that is starting a new trip is more likely to choose a higher concentration (shorter) path. Eventually, most ants are expected to converge to the dominant (shortest) path between the two points. The algorithm uses this pattern to find the shortest path to a goal using probability based on the frequency of paths taken.
The goal of our team’s research is to create a small-scale application of this algorithm using Propeller Activity Bots. Using these small robots, as well as additional hardware and sensors, we aim to investigate the practical application of this algorithm, as well as investigate the feasibility of use in larger scale scenarios, such as transportation, delivery, and in mapping paths in disrupted terrains for natural disaster relief first responders. By limiting ourselves to a small-scale example, we hope to identify the bare-bone necessities for such an implementation of the ACO Algorithm, which have not previously been evident in computational simulations. Through our research we hope to engineer a basic model of practical implementation for the use of those wishing to make use of these concepts in the future.
Recommended Citation
Roggow, Aaron W.; Desmond, Danielle E.; Greenhagen, Chase M.; and Krentz, Timothy S., "Robotic Application of the Ant Colony Optimization Algorithm" (2015). Symposium on Undergraduate Research and Creative Expression (SOURCE). 438.
https://scholar.valpo.edu/cus/438
This is our poster which we will be displaying during the Celebration of Undergraduate Scholarship
Biographical Information about Author(s)
Aaron Roggow is a sophomore computer engineering student from Edwardsville, Illinois. He is interested in mechatronics and visualization systems. Danielle Desmond is a sophomore computer engineering student from Glenview, Illinois. She is interested in software development and engineering management. Chase Greenhagen is a sophomore computer engineering student from Waverly, Minnesota. He is interested in mechatronics and artificial intelligence. Tim Krentz is a junior electrical engineering student from Wheaton, Illinois. He is interested in controls and embedded systems.