Papers
24
Total Citations
580
H-Index
10
About
Gary McMurray is a robotics and automation researcher whose work spans two deeply interconnected domains: advanced visual servoing for robotic control and intelligent automation in food and agricultural processing. He is perhaps best known for his pioneering development of uncalibrated visual servoing techniques, which enable robots to track and manipulate objects using vision feedback without requiring precisely calibrated kinematic or camera models. His dynamic quasi-Newton and Broyden's method approaches, introduced across a series of influential papers from 1999 to 2004, have collectively garnered over 300 citations, establishing foundational methodology in model-independent robotic control. McMurray has translated these capabilities into real-world applications, including autonomous leaf-picking systems using deep learning and visual servoing, and robotic bio-material cutting, where his two-part series on pressing-and-slicing mechanics advanced force modeling and control for food processing robotics. His sustained focus on poultry industry automation—spanning deboning systems and AI-driven broiler and breeder management—demonstrates a career-long commitment to bringing intelligent robotics into challenging, unstructured industrial environments. His 2022 work on AI and sensors in poultry management reflects his continued relevance at the intersection of agricultural technology and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Uncalibrated Dynamic Visual Servoing226 citations · 2004
- 2A dynamic quasi-Newton method for uncalibrated visual servoing75 citations · 2003
- 3
- 4Autonomous Leaf Picking Using Deep Learning and Visual-Servoing40 citations · 2016
- 5A dynamic Jacobian estimation method for uncalibrated visual servoing40 citations · 1999
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- 9Intelligent automation of bird deboning16 citations · 2012
- 10Slicing Cuts on Food Materials Using Robotic‐Controlled Razor Blade11 citations · 2011