Learning-Aided Kalman Tracking in Biased Dynamic Systems: The Case of Cable-Driven Robots for Surgery

In cable-driven robots, the actuation is transmitted via long cables for control and manipulation of the end-effectors. The cables introduce biases due to non-linear tension, creating a challenge for accurate modeling and localization. This paper presents a novel data-driven algorithm that addresses these biases in partially known dynamic systems, focusing on cable-driven robots. The proposed approach integrates classic Kalman filtering with deep learning to enhance tracking and overcome the limitations of existing state-space modeling. Our algorithm learns from data to explicitly track the robot's end-effector, while implicitly tracking its cable-induced bias. We demonstrate the effectiveness of the algorithm on two different robotic manipulators: a planar two-link robotic manipulator, and the Raven surgical robot, constituting testbeds for dynamic systems where the bias arises from time-varying cable tension. Our experimental results reveal that while conventional methods like the Extended Kalman Filter struggle with pose tracking due to system biases, our algorithm successfully localizes the robot's end-effector and uncovers underlying biases, showing significant advancements in predictive capabilities and dynamic system understanding.