An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network
The solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution proces...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/7129 |
| _version_ | 1797525804982206464 |
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| author | Yonghua Bai Minzhou Luo Fenglin Pang |
| author_facet | Yonghua Bai Minzhou Luo Fenglin Pang |
| author_sort | Yonghua Bai |
| collection | DOAJ |
| description | The solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution process is complicated. Due to the mapping ability of the neural network, the use of neural networks to solve robot inverse kinematics problems has attracted widespread attention. However, it has slow convergence speed and low accuracy. This paper proposes the FOA optimized BP neural network algorithm to solve inverse kinematics. It overcomes the shortcomings of low convergence accuracy, slow convergence speed, and easy to fall into local minima when using BP neural network to solve inverse kinematics. The experimental results show that using the trained FOA optimized BP neural network to solve the inverse kinematics, the maximum error range of the output joint angle is [−0.04686, 0.1271]. The output error of the FOA optimized BP neural network algorithm is smaller than that of the ordinary BP neural network algorithm and the PSO optimized BP neural network algorithm. Using the FOA optimized BP neural network algorithm to solve the robot kinematics can improve the control accuracy of the robot. |
| first_indexed | 2024-03-10T09:18:12Z |
| format | Article |
| id | doaj.art-bedee355792e4801bc391e7d5cdf0e6b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:18:12Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bedee355792e4801bc391e7d5cdf0e6b2023-11-22T05:24:52ZengMDPI AGApplied Sciences2076-34172021-08-011115712910.3390/app11157129An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural NetworkYonghua Bai0Minzhou Luo1Fenglin Pang2College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213022, ChinaCollege of Mechanical & Electrical Engineering, Hohai University, Changzhou 213022, ChinaCollege of Mechanical & Electrical Engineering, Hohai University, Changzhou 213022, ChinaThe solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution process is complicated. Due to the mapping ability of the neural network, the use of neural networks to solve robot inverse kinematics problems has attracted widespread attention. However, it has slow convergence speed and low accuracy. This paper proposes the FOA optimized BP neural network algorithm to solve inverse kinematics. It overcomes the shortcomings of low convergence accuracy, slow convergence speed, and easy to fall into local minima when using BP neural network to solve inverse kinematics. The experimental results show that using the trained FOA optimized BP neural network to solve the inverse kinematics, the maximum error range of the output joint angle is [−0.04686, 0.1271]. The output error of the FOA optimized BP neural network algorithm is smaller than that of the ordinary BP neural network algorithm and the PSO optimized BP neural network algorithm. Using the FOA optimized BP neural network algorithm to solve the robot kinematics can improve the control accuracy of the robot.https://www.mdpi.com/2076-3417/11/15/7129inverse kinematicsFOA algorithmPSO algorithmBP neural network |
| spellingShingle | Yonghua Bai Minzhou Luo Fenglin Pang An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network Applied Sciences inverse kinematics FOA algorithm PSO algorithm BP neural network |
| title | An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network |
| title_full | An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network |
| title_fullStr | An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network |
| title_full_unstemmed | An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network |
| title_short | An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network |
| title_sort | algorithm for solving robot inverse kinematics based on foa optimized bp neural network |
| topic | inverse kinematics FOA algorithm PSO algorithm BP neural network |
| url | https://www.mdpi.com/2076-3417/11/15/7129 |
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