DOI: 10.5061/dryad.tqjq2bw84
title: Data-driven calibration of RAVEN-II surgical robot with ground truth joint positions

description: Accurate joint position estimation is crucial for the control of
 cable-driven laparoscopic surgical robots like the RAVEN-II. However, any
 slack and stretch in the cable can lead to errors in kinematic estimation,
 complicating precise control. This work proposes an efficient data-driven
 calibration method, requiring no additional sensors post-training. This
 dataset was collected from a RAVEN-II surgical robot, including different
 calibration trajectories, 6-hour continuous idle, unloaded, and loaded
 operating. Ground truth joint positions for positional joints are also
 collected from high-resolution optical encoders.
description: Please refer to our paper "Efficient Data-driven Joint-level
 Calibration of Cable-driven Surgical Robots" for more
 details. This data set is collected on a RAVEN-II
 surgical robot. Only the left arm of the robot was used.
 Ground truth of joint positions Avago Technologies AEDA- 3300
 encoders were installed on the rotational joints 1 and 2, with a
 resolution of 80000 PPR. Mercury II 1600 was installed on the
 translational joint 3, with a resolution of 5 µm. The external encoders
 were registered during the initialization of RAVEN-II to register the
 offsets.  Robot states "ravenstate" are also
 recorded, which includes time, pose, velocity, force, torque, and so
 on. The robot states and ground truth joint positions
 are synchronized and the frequency is around 30 Hz.
 This dataset is collected for data-driven calibration of
 cable-driven surgical robots. Training trajectories are zig-zag
 trajectories for their even distribution in the workspace, while testing
 trajectories are random trajectories to better suggest the generalization
 ability of the trained models.
description: # Data-driven Calibration of RAVEN-II Surgical Robot with Ground Truth
 Joint Positions
 [https://doi.org/10.5061/dryad.tqjq2bw84](https://doi.org/10.5061/dryad.tqjq2bw84) ## Description of the data and file structure Please do not hesitate to contact Haonan Peng ([penghaonan1993@gmail.com](mailto:penghaonan1993@gmail.com)) if you need any help using this dataset. All code about utilizing this dataset, including neural networks and regressions can be found at GitHub repo:  [https://github.com/HaonanPeng/Efficient-Data-driven-Joint-level-Calibration-of-Cable-driven-Surgical-Robots](https://github.com/HaonanPeng/Efficient-Data-driven-Joint-level-Calibration-of-Cable-driven-Surgical-Robots) as well as index, preprocessing. Each recording .zip file contains training and testing trajectories as .csv files. "rand1200" in the file name indicates this is a random trajectory, otherwise indicating a zig-zag trajectory. "x", "y", or "z" in the file name indicates the direction of zig-zag trajectories, wherein "x - Joint 1", "y - Joint 2", "z - Joint 3", there can also be diagonal directions such as "xy - Joint 1-2". Numbers "05, 03, 025, 02, 016" in the file name means the sparsity of the zig-zag trajectory of 0.5, 0.333, 0.25, 0.2 and 0.1667. There may also be direction and/or sparsity notes for random trajectories. These notes for random trajectory indicate these random trajectories are recorded close to the zig-zag trajectories with these notes, which is to prevent the testing trajectories from being recorded too far away from the training zig-zag, causing a time effect. * Recording 1 has training zig-zag trajectories with different direction, such as Joint 1, Joint 2, Joint 1-2, and so on. * Recording 2 has training zig-zag trajectories with different sparsity. * Recording 3 has has 6-hour testing trajectories in indleness, unloaded, and 500g loaded configuration, to study the time effects. * Recording 4 has homing procedure during testing. And the 2 training sets, one has homing while one does not, to study the homing effects. The column index of the dataset followed, more details can be found in our paper: \# left arm only ---------------------------- np.arange(1,2), # time stamp of ground truth  np.arange(2,5), # ground truth joint position np.arange(5,6), # time stamp of CRTK joint position, usually not used np.arange(6,12), # joint position of CRTK joint position, usually not used, this is the same as the robot state joint position in the following sections np.arange(12,13), # time stamp of robot state "ravenstate", all the following indice are from robot state np.arange(13,21), # joint position np.arange(29,30), # run level np.arange(30,31), # sub-level np.arange(31,32), # last sequence np.arange(32,33), # arm type np.arange(34,37), # end-effector position np.arange(40,49), # end-effector orientation np.arange(58,67), # end-effector orientation desired np.arange(76,79), # end-effector position desired np.arange(82,98), # encoder value np.arange(98,106), # motor dac value np.arange(114,122), # motor torque np.arange(130,138) # motor position np.arange(146,154), #  motor velocity np.arange(162,170), # joint velocity np.arange(178,186), # motor position desired np.arange(194,202), # joint position desired np.arange(210,211), # grasper desired np.arange(212,228), # encoder offset np.arange(228,234), # Jacobian velocity np.arange(240,246) # Jacobian force ## Code/software Coda available at: [https://github.com/HaonanPeng/Efficient-Data-driven-Joint-level-Calibration-of-Cable-driven-Surgical-Robots](https://github.com/HaonanPeng/Efficient-Data-driven-Joint-level-Calibration-of-Cable-driven-Surgical-Robots) ## Usage Note This DRYAD dataset contains robot states and ground truth joint positions accompanying the 2024 manuscript "Efficient Data-driven Joint-level Calibration of Cable-driven Surgical Robots" by Peng, H et al., to be published in npj Robotics. ## Access information Data was derived from the following sources: * RAVEN-II surgical robot