Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares
As we advance towards the future of the smart manufacturing industry, our research focuses on enhancing manipulator technology. Inverse kinematics is a key component of robotic arm control, yet many existing methods struggle to achieve high performance when dealing with high-precision target points...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10812731/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841550769716723712 |
|---|---|
| author | Shusheng Yu Gongquan Tan |
| author_facet | Shusheng Yu Gongquan Tan |
| author_sort | Shusheng Yu |
| collection | DOAJ |
| description | As we advance towards the future of the smart manufacturing industry, our research focuses on enhancing manipulator technology. Inverse kinematics is a key component of robotic arm control, yet many existing methods struggle to achieve high performance when dealing with high-precision target points and highly redundant robotic arms. In this paper, we propose a novel solution to the inverse kinematics problem by combining Proximal Policy Optimization (PPO) with the Damped Least Squares (DLS) method, forming the Multistep PPO-DLS Inverse Kinematics (MPDIK) algorithm. The algorithm was trained and tested in the PyBullet virtual environment, using random seven-dimensional position and pose target points. The MPDIK algorithm demonstrated outstanding performance, with the end effector achieving a distance error of less than 0.1 mm and an orientation error of less than 0.001°. Additionally, it exhibited excellent stability and fast convergence, with a post-training task completion success rate of 98.37% and an average of 20.68 time steps per task. This represents a significant improvement over existing methods, such as PPO and DLS, and demonstrates universal applicability. Our experiments also revealed that this method holds great potential for improving both the accuracy and real-time application capabilities of robotic systems. |
| format | Article |
| id | doaj-art-679ee60020904d03952888bbf15a985a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-679ee60020904d03952888bbf15a985a2025-01-10T00:01:38ZengIEEEIEEE Access2169-35362025-01-01134857486810.1109/ACCESS.2024.352153910812731Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least SquaresShusheng Yu0https://orcid.org/0009-0009-1626-7942Gongquan Tan1School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, Sichuan, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, Sichuan, ChinaAs we advance towards the future of the smart manufacturing industry, our research focuses on enhancing manipulator technology. Inverse kinematics is a key component of robotic arm control, yet many existing methods struggle to achieve high performance when dealing with high-precision target points and highly redundant robotic arms. In this paper, we propose a novel solution to the inverse kinematics problem by combining Proximal Policy Optimization (PPO) with the Damped Least Squares (DLS) method, forming the Multistep PPO-DLS Inverse Kinematics (MPDIK) algorithm. The algorithm was trained and tested in the PyBullet virtual environment, using random seven-dimensional position and pose target points. The MPDIK algorithm demonstrated outstanding performance, with the end effector achieving a distance error of less than 0.1 mm and an orientation error of less than 0.001°. Additionally, it exhibited excellent stability and fast convergence, with a post-training task completion success rate of 98.37% and an average of 20.68 time steps per task. This represents a significant improvement over existing methods, such as PPO and DLS, and demonstrates universal applicability. Our experiments also revealed that this method holds great potential for improving both the accuracy and real-time application capabilities of robotic systems.https://ieeexplore.ieee.org/document/10812731/Manipulatorsrobot kinematicsreinforcement learningartificial intelligence |
| spellingShingle | Shusheng Yu Gongquan Tan Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares IEEE Access Manipulators robot kinematics reinforcement learning artificial intelligence |
| title | Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares |
| title_full | Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares |
| title_fullStr | Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares |
| title_full_unstemmed | Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares |
| title_short | Inverse Kinematics of a 7-Degree-of-Freedom Robotic Arm Based on Deep Reinforcement Learning and Damped Least Squares |
| title_sort | inverse kinematics of a 7 degree of freedom robotic arm based on deep reinforcement learning and damped least squares |
| topic | Manipulators robot kinematics reinforcement learning artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10812731/ |
| work_keys_str_mv | AT shushengyu inversekinematicsofa7degreeoffreedomroboticarmbasedondeepreinforcementlearninganddampedleastsquares AT gongquantan inversekinematicsofa7degreeoffreedomroboticarmbasedondeepreinforcementlearninganddampedleastsquares |