Low-cost double pendulum for high-quality data collection with open-source video tracking and analysis

[Display omitted] The double pendulum is a system that manifests fascinating non-linear behavior. This made it a popular tool in academic settings for illustrating the intricate response of a seemingly simple physical apparatus, or to validate tools for studying nonlinear phenomena. In addition, the...

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Bibliographic Details
Published in:HardwareX Vol. 8; p. e00138
Main Authors: Myers, Audun D., Tempelman, Joshua R., Petrushenko, David, Khasawneh, Firas A.
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01-10-2020
Elsevier
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Summary:[Display omitted] The double pendulum is a system that manifests fascinating non-linear behavior. This made it a popular tool in academic settings for illustrating the intricate response of a seemingly simple physical apparatus, or to validate tools for studying nonlinear phenomena. In addition, the double pendulum is also widely used in several modeling applications including robotics and human locomotion analysis. However, surprisingly, there is a lack of a thoroughly documented hardware that enables designing, building, and reliably tracking and collecting data from a double pendulum. This paper provides comprehensive documentation of a research quality bench top double pendulum. The contributions of our work include (1) providing detailed CAD drawings, part lists, and assembly instructions for building a low friction double pendulum. (2) A new tracking algorithm written in Python for tracking the position of both links of the double pendulum. This algorithm measures the angles of the links by examining each frame, and computes uncertainties in the measured angles by following several trackers on each link. Additionally, our tracking algorithm bypasses the data transmission difficulties caused by instrumenting the bottom link with physical sensors. (3) A derivation of the equations of motion of the actual physical system. (4) A description of the process (with provided Python code) for extracting the model parameters—e.g., damping—with error bounds from physical measurements.
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ISSN:2468-0672
2468-0672
DOI:10.1016/j.ohx.2020.e00138