Scott McGhee
Papers
1
Total Citations
31
H-Index
1
About
Scott McGhee’s research centers on the control and optimization of robotic manipulators, with a particular focus on redundant systems—robots with more degrees of freedom than necessary for a given task. His most-cited work, "Probability-Based Weighting of Performance Criteria for Redundant Manipulators" (1997, 31 citations), introduced a novel framework for dynamically weighting performance criteria, such as joint limits and obstacle avoidance, using probabilistic methods. This approach allowed for more adaptive and efficient motion planning in complex environments, addressing a key challenge in robotics: how to balance multiple, often conflicting, objectives in real time. By shifting from static to probability-based weighting, McGhee’s contribution provided a more flexible and robust solution for redundant manipulators, influencing subsequent research in robot kinematics and control. While his citation count reflects a focused but impactful body of work, this paper remains a reference point for scholars exploring intelligent weighting strategies in robotic systems. McGhee’s work underscores the importance of probabilistic reasoning in advancing autonomous manipulation, offering practical insights for both academic research and industrial applications.
Research Focus
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- 1