Dr. Mae Seto

A harsh robotic environment is defined as one where it is difficult to sense, communicate, control, move around or otherwise operate a cyberphysical system – for which robots are one example. The harshest environments include underwater and space (Earth orbit). Traditional control theory constructs a plant model to design, develop and test a proposed control methodology. Uncertainties in the plant are always acknowledged as caveats when qualifying the performance and efficacy of a methodology. The robot’s interaction with an unstructured dynamic environment precludes a well-defined plant. This presentation will look at the use cases of underwater and space environments and note similarities and differences in their harsh environment control challenges. Finally, promising research directions and examples are presented.

Biography:

Dr. Mae Seto is an Associate Professor in Dalhousie’s Mechanical Engineering Department and the Irving Shipbuilding Research Chair in Marine Engineering and Autonomous Systems. She received the PhD (Mechanical Engineering - Aerodynamics and Control), from the University of British Columbia in 1996. After being an NSERC Postdoctoral Industrial Research Fellow (1996 - 1998), Dr. Seto worked in private industry (ISE Research Ltd., Pt. Coquitlam, Canada) for six years on research and development projects related to autonomous underwater vehicles. Dr. Seto moved to Nova Scotia to work as a defence scientist for Defence R&D Canada and joined the Dalhousie University as an Associate Professor in 2017. Dr. Seto’s research focus is the development of intelligent (unmanned) autonomous systems, and particularly for deployment in difficult environments like marine and under-ice. Her Intelligent Systems Laboratory is multi-disciplinary and works at the intersection of engineering (electrical, mechanical) and computer science to enable autonomous systems to operate at long ranges from an operator for extended durations in harsh and dynamic environments like oceans and space. This is achieved by developing and using tools that include adaptive deliberation, machine learning, decision-making, optimization, autonomous mission-planning.