Michael Fennel
Papers
8
Total Citations
39
H-Index
3
About
Michael Fennel is a robotics researcher whose work centers on human-robot interaction, haptic feedback, and real-time control systems for manipulators and mobile platforms. His major contributions include pioneering a haptic-guided path generation method for remote car-like vehicles (18 citations), which enables intuitive manual intervention without specialized input devices, and developing a novel haptic rendering technique for programming arbitrary serial manipulators by replacing physical master devices with digital twins. Fennel has also advanced kinematic state estimation for robotic manipulators through calibration-free IMU-based methods (4 citations) and observability-driven sensor placement strategies (3 citations), addressing critical needs for precise control in hazardous environments. His work on the RTCF framework (3 citations) provides a modular, real-time control solution seamlessly integrated with ROS, while his research into intention estimation using recurrent neural networks and mobile depth camera tracking for augmented reality applications demonstrates a commitment to making robotics more accessible and intuitive. With applications in remote decontamination and collaborative manufacturing, Fennel’s research—garnering over 35 citations—bridges the gap between autonomous systems and human operators, emphasizing safety, immersion, and ease of use.
Research Focus
Key Achievements
Top Papers
- 1Haptic-Guided Path Generation for Remote Car-Like Vehicles18 citations · 2021
- 2Haptic Rendering of Arbitrary Serial Manipulators for Robot Programming5 citations · 2021
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- 5RTCF: A framework for seamless and modular real-time control with ROS3 citations · 2021
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