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

Audun D Myers1, Joshua R Tempelman1, David Petrushenko1

  • 1Department of Mechanical Engineering at Michigan State University, Department of Mechanical Engineering at Virginia Tech, United States.

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|May 2, 2022
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Summary

This study presents a fully documented, low-friction double pendulum hardware system. It includes a novel Python tracking algorithm and methods for parameter extraction, aiding nonlinear dynamics research.

Keywords:
Double pendulumOcclusionsPendulumTracking

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Area of Science:

  • Physics
  • Mechanical Engineering
  • Robotics

Background:

  • The double pendulum is a classic system for studying nonlinear dynamics.
  • It has applications in robotics and human locomotion analysis.
  • Existing hardware lacks comprehensive documentation for research purposes.

Purpose of the Study:

  • To provide detailed documentation for building a research-quality benchtop double pendulum.
  • To introduce a novel Python-based tracking algorithm for precise data collection.
  • To enable reliable data acquisition and model parameter extraction for nonlinear systems.

Main Methods:

  • Detailed CAD drawings, part lists, and assembly instructions for a low-friction double pendulum.
  • Development of a Python tracking algorithm using frame analysis and multiple trackers.
  • Derivation of the system's equations of motion.
  • A Python-based method for extracting model parameters with error bounds.

Main Results:

  • Successful construction of a low-friction double pendulum.
  • A robust tracking algorithm capable of measuring link angles and uncertainties.
  • The algorithm bypasses sensor challenges on the bottom link.
  • Validated methods for parameter extraction from experimental data.

Conclusions:

  • The provided documentation and tools facilitate research in nonlinear dynamics.
  • This work addresses the need for accessible and reliable double pendulum hardware.
  • Enables accurate data collection and model validation for complex physical systems.