Kinematics-Inertial Fusion for Localization of a 4-Cable Underactuated Suspended Robot Considering Cable Sag

Abstract

Suspended Cable-Driven Parallel Robots (SCDPR) have intriguing capabilities on large scales but still have open challenges in precisely estimating the end-effector pose. The cables exhibit a downward curved shape, also known as cable sag which needs to be accounted for in the pose estimation. The catenary equations can accurately describe this phenomenon but are only accurate in equilibrium conditions. Thus, pose estimation for large-scale SCDPR in dynamic motion is an open challenge. This work proposes a real-time pose estimation algorithm for dynamic trajectories of SCDPRs, which is accurate over large areas. We present a novel approach that considers cable sag to reduce the estimation error for large scales while also employing an Inertial Measurement Unit (IMU) to improve estimation accuracy for dynamic motion. Our approach reduces the RMSE to less than a third compared to standard methods not considering cable sag. Similarly, the inclusion of the IMU reduces the RMSE in dynamic situations by 40% compared to non-IMU aided approaches considering cable sag. Further-more, we evaluate our Extended Kalman Filter (EKF) based algorithm on a real system with ground truth pose information.