Dr. Suranga Hettiarachchi is an associate professor of computer science at Indiana University Southeast. He teaches courses in computer programming, software systems, and robotics. He has been an educator in field of computer science for the last 20 years. His research interests include swarm robotics, multi-agent Systems, Physicomimetics, distributed robotics architectures, evolutionary adaptive learning, search algorithms, and swarm intelligence. He has authored, published, presented, reviewed, and edited numerous articles in his research area. He has won awards for his scholarly work. He is the founder of distributed robotics laboratory at Indiana University Southeast. He is a member of IEEE.
- University of Wyoming, Laramie, Wyoming, USA
- Doctor of Philosophy, Major in Computer Science
- University of Wyoming, Laramie, Wyoming, USA.
- Bachelor of Science, Major in Management Information Systems
Programming Languages and Techniques, Data Structures, Algorithms, Robotics, and Data Science.
My research interests are Swarm Robotics, Multi-Agent Systems, Physicomimetics, Distributed Robotics, Robotic Applications, Evolutionary Learning, Search Algorithms, Adaptive Learning, Swarm Intelligence, and Data Science.
- Distributed Real-Time Evolution of Human-guided Discovery Scouts - with Dimitri Zarzhitsky (Pacific Northwest National Labs) and Alan Grant (IUS)
- Provability in Engineering a Hexagonal Seven-Agent Swarm - with Sanza Kazadi (Illinois Math and Science Academy)
With Undergraduate Students:
Data Mining and Modeling of Crime Statistics for The City of Louisville Using Tableau
David Phaire (Informatics)
The collection and processing of "big data" from everywhere has increasingly become more important and of interest to police and citizens of Louisville, KY. This project examines the effects of policing and crime frequencies based on population demographics of the City of Louisville. The empirical results are based on the data drawn from the U.S. Census Bureau and Louisville Metro Police Crime Statistics database. This study looks at if increased policing reduces crimes in affluent areas versus less affluent areas of the city and if there is a correlation between education levels of citizens and the police officers. We use Excel and Tableau as the analytical tools to verify our hypothesis and visualize data.
Joshua Donahoe, John Stratton, and Albert Rhodes (CS)
In this project, we guide an autonomous Drone to avoid projectiles. Using a Kinect camera, a computer program generates a 3D point cloud mapping of the environment surrounding the drone. The computer approximates the trajectory of a thrown projectile using basic physics formulas and transmits the possible collision point of the projectile to the drone to evade the projectiles if a collision is imminent.
Drone versus Robot, Hide and Seek
David Allgeier, Tony Freitas, and Jeremy Williams (CS)
We design and implement a quadcopter drone to play a game of hide and seek with a land-based mobile robot. The task of the mobile robot is to hide within an enclosed environment and wait. The task of the drone is to search for the mobile robot, using a combination of infrared and sonic sensors to map the enclosure as it scans the environment. The drone eliminates possible hiding places until it finds the robot.
Kristopher O'Bryan and Ryan Tate (CS)
The purpose of our project is to design and implement an automated robotic system that can engage in table tennis volleys with a human player. Our robotic system consists of a robotic arm and cameras that is capable of tracking the ball's trajectory. A software program analyzes the still images from cameras to estimate the trajectory of the ball and informs the robotic arm the point of contact.
Checkers Robot Arm
John Karr, Alan Grant, and Timothy Spalding (CS)
We present a robotic system capable of playing a simplified version of checkers against a human opponent. This system is composed of a computer vision system, a decision-making system, and a control system for a robotic arm. The computer vision system is used in determining the current game state. The decision system determines the most desirable move the robot should make for a win. The control system determines the robotic arm manipulation in order to perform the chosen move. These three subsystems work together as an autonomous checkers playing robot.
XFM Vending Machine
Joseph Olin, Branden Wagner, and Michael Roark (CS)
This project designs and implements a robotic system that acts as a bar waiter. We use a robotic arm mounted on a mobile robot to bring consumables to customers who are sitting at the bar counter. The robot decides the delivery of consumables based on customer input. We accomplish a robot control software architecture utilizing Arduino based sensors and effectors. Our project demonstrates how a familiar task, such as tending bar, can be handled by a low cost intelligent robot.
Lager Inventory System
Kuan-Yi Wu, David Paller, and Ian Floro (CS Capstone Project)
We design and implement a web-based inventory control system for the Distributed Robotics Laboratory (DRL) at IU Southeast. This application allows the DRL to maintain laboratory inventory of robot parts and track the parts that are available or loaned out to students. This is a user-friendly web-based application with a database that allows the end-user to add, delete, and modify records. It also allows the user to generate multiple reports. This approach is durable and efficient compared to maintaining inventory on a spreadsheet. We utilize multiple software tools and platforms to accomplish this task.
- Twitter Crawling Sentiment Analysis in Relation to Weather - with Matthew Ballard (IUS) (paper in preparation)
- Use Configurable Output Classifier for AutoDock - with Joseph Dukes and Victor Waingeh (IUS)
Teaching Resource Websites
- M. Li, C. Qiu, J. Park, D. Chan, J. Jeon, J. Na, C. Wong, B. Zhao, E. Chang, S. Kazadi, S. Hettiarachchi. (2017). Generating swarm solution classes using the Hamiltonian Method of swarm design. 9th International Conference on Agents and Artificial Intelligence, 1 (1), 145-152.
- Kleczynski, S., M. Rayner, and S. Hettiarachchi. (2014). Autonomous search and retrieval. The Journal of Computing Science in Colleges, 29 (5), 164-170.
- Riddle, J., R. Hughes, N. Biefeld, and S. Hettiarachchi. (2012). A user friendly software framework for mobile robot control. The 23rd Midwest Artificial Intelligence and Cognitive Science Conference, 0074-841 (4), 44-49.
- S. Hettiarachchi.. (2012). Physicomimetics for mobile robot obstacle avoidance.. he 13th International Conference on Software Engineering, Artificial Intelligence, Network- ing and Parallel/Distributed Computing, 978-1-4673-2120-4 (5), 444-450.
- Hettiarachchi S., Spears W.. (2009). Distributed Adaptive Swarm for Obstacle Avoidance.. International Journal of Intelligent Computing and Cybernetics - Special Issue in Swarm Robotics, 2 (4), 644-671.
- S. Hettiarachchi. Editors: Spears, William M., Spears, Diana F.. (2011). In Physics-based Swarm Intelligence: From Theory to Practice.. Berlin Heidelberg: Springer.