Jenny M. Lewis

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About

Jenny M. Lewis is a robotics researcher whose work has made significant contributions to the field of industrial robot calibration and accuracy improvement. Her research primarily focuses on kinematic modeling, autonomous calibration methodologies, and the application of neural networks to enhance robot precision in real-world manufacturing environments. Lewis's most influential contribution, "A New Method for Autonomous Robot Calibration" (2002), has garnered 49 citations and introduced a practical on-site calibration approach using a trigger probe as a manipulator extension, enabling kinematic identification through internal joint sensor measurements — a notable advancement for industrial deployment. Her complementary work on neuro-accuracy compensation demonstrated the power of combining analytical kinematic models with neural networks to accurately map robot world-space coordinates to joint transducer readings, accumulating 13 citations. This hybrid modeling approach offered a compelling alternative to purely model-based calibration strategies. Her earlier investigation into neural network solutions for the robot inverse calibration problem, published in 1994, laid important groundwork by characterizing end-effector pose errors across a six-degree-of-freedom PUMA robot. Together, these works reflect a sustained research career bridging classical robotics theory with intelligent computational methods, providing practical tools that improve robot accuracy in demanding industrial settings.