Heshan Fernando is a Ph.D. candidate in the Department of Mechanical and Materials Engineering at Queen’s University, Kingston, Canada. He is interested in the research and development of intelligent systems for industrial applications–robotic machines and autonomous processes that can learn and adapt to operate reliably in dynamic and complex environments. As a member of the Offroad Robotics research group, Heshan is currently investigating methods of control and learning for autonomous excavation using robotic wheel loaders. Prior to this, Heshan received the B.Eng (Hons) degree in Mechanical engineering from the University of Victoria and the M.A.Sc. degree in Mechanical Engineering from Queen’s University.
APSC 200 – Engineering Design and Practice II (fall 2018)
MINE 471 – Mine-Mechanical Design Project (winter 2018)
MINE 472 – Mining Systems, Automation, and Control (winter 2016 to 2018)
MECH 350 – Automatic Controls Labs (winter 2013 to 2016)
MECH 452 – Mechatronics (fall 2012 to 2015, 2017-2018)
H. Fernando, J. A. Marshall, and J. Larsson. Iterative learning-based admittance control for autonomous excavation. To appear in Journal of Intelligent & Robotic Systems. Accepted January 29, 2019. DOI: 10.1007/s10846-019-00994-3
H. A. Fernando and B. W. Surgenor. An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine. In Robotics and Computer Integrated Manufacturing, vol. 43, Special issue: extended papers selected from FAIM 2014, pp. 79–88, February 2017.
K. Hughes, H. A. Fernando, G. Szkilnyk., B. W. Surgenor and M. Greenspan. Video event detection for fault monitoring in assembly automation. In International Journal of Intelligent Systems Technologies and Applications, vol. 14, nos. 1 and 2, pp. 106-113, 2013.
Conference Papers (Fully Refereed)
H. A. Fernando, J. A. Marshall, H. Almqvist and J. Larsson. Towards controlling bucket fill factor in robotic excavation by learning admittance control setpoints. 11th Conference on Field and Service Robotics, Zurich, September 2017.
H. A. Fernando and B. W. Surgenor. An artificial neural network based on adaptive resonance theory for fault classification on an automated assembly machine. In Proceedings of the 24th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), San Antonio, Texas, May 2014.
H. A. Fernando, V. Chauhan and B. W. Surgenor. Image-based versus signal-based sensors for machine fault detection and isolation. In Proceedings of the ASME 12th Biennial Conference on Engineering Systems Design and Analysis (ESDA), Copenhagen, Denmark, June 2014.
H. A. Fernando and B. W. Surgenor. An RFID-based automated warehouse project for a course in mechatronics. In Proceedings of the 2nd International Conference on Mechanical Engineering and Mechatronics (ICMEM), Toronto, Ontario, August 2013.
H. A. Fernando, K. Hughes, G. Szkilnyk., B. W. Surgenor and M. Greenspan. Video event fault detection with STVs: application to a high speed assembly machine. In Proceedings of the 41st North American Manufacturing Research Conference (NAMRC), Madison, Wisconsin, June 2013.
H. A. Fernando, J. Siriwardana and S. Halgamuge. Can a datacenter heat-flow model be scaled down? In Proceedings of the IEEE 6th International Conference Information and Automation for Sustainability (ICIAfS), Beijing, China, September 2012.
Conference Papers (Abstract Refereed)
H. A. Fernando, C. Lounsbury and B. W. Surgenor. Integrating RFID technology into a course in mechatronics. In Proceedings of the International Conference on Engineering Education and Research (ICEER), Hamilton, Ontario, August 2014.
V. Chauhan, H. A. Fernando and B. W. Surgenor. Effects of illumination techniques for machine vision inspection for automated assembly machines. In Proceedings of the Canadian Society for Mechanical Engineering (CSME) International Congress, Toronto, Ontario, June 2014.
Conference Presentations (Non-Author)
J. Waldie, B.W. Surgenor and B. Dehghan. Fuzzy PID and contour tracking as applied to position control of a pneumatic gantry robot. ASME/BATH Symposium on Fluid Power and Motion Control (FPMC), Sarasota, Florida, October 8, 2013.
Fernando, H., Artificial Neural Networks for Fault Detection and Identification on an Automated Assembly Machine, M.ASc. Thesis, Department of Mechanical Engineering, Queen’s University, Kingston, ON, May 2014 (advisor: Brian Surgenor).
Fernando, H, Alternative Wind Turbine Technologies, B.Eng (Hons) Thesis, Department of Mechanical Engineering, University of Victoria, Victoria, BC, May 2011 (advisor: Curran Crawford).