Forward Kinematics of a Cable-Driven Parallel Robot with Pose Estimation Error Covariance Bounds
The purpose of my work is to develop an algorithm that uses covariance bounds to measure the accuracy of forward kinematics on a cable-driven parallel robot. This is done with Constrained Attitude Parameterizations, which is an extension of previous work that studied Unconstrained parameterizations. Previous studies have been limited in data and analysis; this work can help with sensor fusion methods to ensure the accuracy of the pose estimate of the robot’s payload.
MATLAB was used intensively for the data acquisition; a pair of loop-closure equations and a pair of stopping criteria were compared when using constrained parameterizations to observe the accuracy of the covariance measurements through several thousand simulations, known as a Monte-Carlo method. The statistics of the covariance measurements were compared and analyzed.
It was concluded that the code created can accurately measure the covariance of the pose estimate without Monte-Carlo simulations. Due to the extensive simulations, the deviations in the data were significantly small, and the percent and average differences were within a suitable margin of error.
Extensions to this work include adding additional noise and error to the system and performing experimental data with a physical cable robot
