Sean McGhee

University of Tennessee at Knoxville, DEVCOM Army Research Laboratory

Papers

Total Citations

H-Index

About

Sean McGhee’s research lies at the intersection of robotics, computer vision, and autonomous systems, with a focus on solving fundamental challenges in perception and control. His most cited work, “Probability-based weighting of performance criteria for a redundant manipulator” (2002, 22 citations), introduced a statistically grounded method for simultaneously optimizing multiple performance criteria—a critical advance for robotic arms operating in complex, unstructured environments. By developing a variable weighting technique that accounts for statistical variance, McGhee addressed a long-standing limitation in redundancy resolution. His 2011 paper, “The perception problem and the impact on robotics and computer vision” (18 citations), posed a provocative question: despite exponential gains in computing power, why has machine perception lagged so far behind? This work critically examined the disconnect between hardware speed and perceptual intelligence, influencing subsequent research on sensor integration and real-time scene understanding. McGhee’s contributions have shaped how roboticists think about balancing multiple objectives and the persistent gap between computational capacity and meaningful environmental interaction. His work remains essential reading for students and researchers tackling redundancy resolution and the perception bottleneck in autonomous systems.