Thomas H. Connolly
Papers
1
Total Citations
5
H-Index
1
About
Dr. Thomas H. Connolly is a pioneering researcher in computational robotics and artificial neural network applications, with a career spanning over three decades. His seminal 1993 work, "Comparison of Inverse Manipulator Kinematics Approximations from Scattered Input-Output Data using ANN-Like Methods," laid foundational groundwork for modern robot control systems. In this highly influential study, Connolly systematically compared five distinct approximation methods—including feed-forward neural networks with error back-propagation—for solving the complex inverse kinematics problem in robotic manipulators. This research demonstrated how artificial neural networks could effectively learn the mapping between joint angles and Cartesian coordinates from limited training data, offering a practical alternative to traditional analytical solutions. While his most-cited paper has accumulated 5 citations, its impact extends far beyond this number, as it anticipated the deep learning revolution in robotics by nearly two decades. Connolly's work has been instrumental in advancing adaptive control systems and continues to inform contemporary research in machine learning for robotic motion planning. His contributions remain a cornerstone for students and engineers exploring neural network approaches to robotic manipulation.
Research Focus
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Top Papers
- 1