An efficient solver for the inverse kinematics of cable-driven manipulators with pure rolling joints using a geometric iterative approach
This repository contains the source code for the paper An efficient solver for the inverse kinematics of cable-driven manipulators with pure rolling joints using a geometric iterative approach. This paper has been accepted to Mechanism and Machine Theory.
@article{YANG2024105611,
title = {An efficient solver for the inverse kinematics of cable-driven manipulators with pure rolling joints using a geometric iterative approach},
journal = {Mechanism and Machine Theory},
volume = {196},
pages = {105611},
year = {2024},
issn = {0094-114X},
doi = {https://doi.org/10.1016/j.mechmachtheory.2024.105611}Haotian Yang currently working toward the M.S. degree in Electronic Information (Artificial Intelligence) with Center of Intelligent Control and Telescience, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. His research interests include kinematics and dynamics of robots, robot manipulation, and deep reinforcement learning.
TPGI is implemented in MATLAB R2020a. The Robotics Toolbox for MATLAB (10.4 version) is used, which can be downloaded from https://petercorke.com/toolboxes/robotics-toolbox/.
The source code of TPGI and a test program are provided in the TPGI_solver folder.
The source code of the KDL method, the SQP method and the TPGI method is given in the Comparison_of_multiple_IK_methods folder. A program for performance comparison between the three algorithms is also provided. In the Runtime_distribution_and_workspace_coverage folder, the program for plotting the runtime distribution is given in two scripts according to the running order.
The Generality_tests folder contains parameters for the four manipulators to be tested, corresponding TPGI solvers, and performance test programs.
The solvers' parameters (e.g., the max runtime) need to be adjusted according to the hardware parameters of the running platform！