Kalman Filter Based Pose Estimation for CDPRs (Cable Driven Parallel Robots)

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Kalman Filter Based Pose Estimation for CDPRs (Cable Driven Parallel Robots)

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2022-04

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Abstract

This work extends previous work by taking the novel a novel extended Kalman filter (EKF) approach for the pose estimation of a cable driven parallel robot (CDPR) and comparing it with a novel Unscented Kalman Filter (UKF) based approach. Both filters fuse accelerometer, rate gyroscope, and winch encoder data together to create a more accurate covariance pose estimate. Both filters are tested on experimental data collected by a six degree of freedom CDPR test bed. The current results show minimal difference between the two filters but offer promising possibilities for future work.

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Faculty Mentor: Ryan Caverly

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This project was sponsored by the University of Minnesota’s Undergraduate Research Opportunities Program (UROP).

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Puri, Neel. (2022). Kalman Filter Based Pose Estimation for CDPRs (Cable Driven Parallel Robots). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/261366.

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