David Bitterolf
Papers
2
Total Citations
6
H-Index
2
About
David Bitterolf is a researcher specializing in robotics and system identification, with a focused expertise in the inertial parameter estimation of industrial manipulators. His work centers on leveraging frequency-domain analysis to extract critical dynamic properties from robotic systems, offering a more robust alternative to traditional time-domain methods. Bitterolf’s major contributions include developing a frequency-based identification routine that utilizes the speed-controlled system’s frequency response to determine inertial parameters—such as mass, center of mass, and inertia tensors—of industrial robots. This approach enhances accuracy and noise resilience, directly improving robot control, simulation, and load handling. His foundational paper, "Frequency-Based Identification of the Inertial Parameters of an Industrial Robot" (2020), has garnered 4 citations, while its extended follow-up (2022) further refines the methodology. Though early in his citation impact, Bitterolf’s work is notable for addressing a practical bottleneck in robotics: the precise calibration of robot dynamics without disassembly. His research is particularly valuable for engineers and researchers seeking to optimize robot performance in manufacturing, where accurate parameter identification enables safer, more efficient automation.
Research Focus
Key Achievements
Top Papers
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