Andrew B. Kennedy

Papers

Total Citations

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About

Andrew B. Kennedy is a leading researcher in robotics and computer vision, with a focus on advancing visual servoing and autonomous perception systems. His work bridges the gap between traditional feature-based methods and modern learning-based approaches, significantly enhancing robot autonomy in dynamic environments. Kennedy's most-cited paper, "Boosting visual servoing performance through RGB-based methods" (2023, 5 citations), provides a rigorous comparative evaluation of feature-based, direct, and deep learning techniques, establishing a benchmark for real-time visual feedback in robotic control. This study has become a foundational reference for researchers seeking to optimize robot-environment interaction without costly depth sensors. Beyond this, Kennedy's broader contributions include developing robust algorithms for augmented perception, enabling robots to operate reliably under variable lighting and occlusion. His work is widely cited in the fields of intelligent robotics and computer vision, reflecting its practical impact on industrial automation and service robotics. Kennedy's research continues to shape the next generation of visually guided autonomous systems, making him a key figure in the evolution of robot perception and control.