Ryan James Caverly — accepted AI-conference papers

2025

Kalman-Filter-Based Pose Estimation of Cable-Driven Parallel Robots Using Cable-Length Measurements with Colored Noise

ICRA 2025

This paper introduces a cable-length-based extended Kalman filter (L-EKF) framework to estimate the end-effector pose of a cable-driven parallel robot (CDPR). The L-EKF fuses end-effector accelerometer and rate gyroscope measurements with cable-length measurements. The main contribution compared to

Cited by 0 Source ↗Scholar ↗

2021

Cable-Driven Parallel Robot Pose Estimation Using Extended Kalman Filtering With Inertial Payload Measurements

Vinh Le Nguyen, Ryan James Caverly

RA-L 2021

This letter introduces two novel extended Kalman filtering (EKF) approaches to fuse payload accelerometer and rate gyroscope data with forward kinematics to estimate the payload pose of a cable-driven parallel robot (CDPR). An Euler-angle-based EKF and a rotation-vector-based multiplicative extended

Cited by 32 Source ↗Scholar ↗