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About

Mark McClelland is a robotics and autonomous systems researcher whose work sits at the intersection of spatial reasoning, robotic navigation, and human-robot interaction. He is best known for pioneering qualitative relational mapping (QRM), a graph-based approach that enables autonomous robots to navigate and map large-scale environments using minimal sensing. Rather than relying on precise metric measurements, his framework encodes qualitative spatial constraints between landmarks, offering a robust and computationally efficient alternative for environments where sensing is limited or unreliable. This foundational work, developed across multiple publications from 2012 to 2016, has found particular application in planetary rover exploration — a domain where communication delays and resource constraints make autonomous decision-making essential. McClelland has also made notable contributions to human factors research, developing probabilistic models to assess whether human operators anticipate future control demands when managing time-delayed remote vehicles, a critical concern in teleoperation. His most cited work, the 2014 paper on QRM with minimal sensing, has garnered 8 citations, with his broader body of work accumulating approximately 29 citations. His research offers valuable tools for next-generation autonomous exploration systems operating in challenging, resource-constrained environments.