Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm
In this study, a Back Propagation (BP) neural network algorithm based on Genetic Algorithm (GA) optimization is proposed to plan and optimize the trajectory of a redundant robotic arm for the upper limb rehabilitation of patients. The feasibility of the trajectory was verified by numerical simulatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4071 |
| _version_ | 1797491674406977536 |
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| author | Xiaohan Qie Cunfeng Kang Guanchen Zong Shujun Chen |
| author_facet | Xiaohan Qie Cunfeng Kang Guanchen Zong Shujun Chen |
| author_sort | Xiaohan Qie |
| collection | DOAJ |
| description | In this study, a Back Propagation (BP) neural network algorithm based on Genetic Algorithm (GA) optimization is proposed to plan and optimize the trajectory of a redundant robotic arm for the upper limb rehabilitation of patients. The feasibility of the trajectory was verified by numerical simulations. First, the collected dataset was used to train the BP neural network optimized by the GA. Subsequently, the critical points designated by the rehabilitation physician for the upper limb rehabilitation were used as interpolation points for cubic B−spline interpolation to plan the motion trajectory. The GA optimized the planned trajectory with the goal of time minimization, and the feasibility of the optimized trajectory was analyzed with MATLAB simulations. The planned trajectory was smooth and continuous. There was no abrupt change in location or speed. Finally, simulations revealed that the optimized trajectory reduced the motion time and increased the motion speed between two adjacent critical points which improved the rehabilitation effect and can be applied to patients with different needs, which has high application value. |
| first_indexed | 2024-03-10T00:52:43Z |
| format | Article |
| id | doaj.art-3edb5b62194a465a98e041e05d55d615 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:52:43Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3edb5b62194a465a98e041e05d55d6152023-11-23T14:48:10ZengMDPI AGSensors1424-82202022-05-012211407110.3390/s22114071Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic AlgorithmXiaohan Qie0Cunfeng Kang1Guanchen Zong2Shujun Chen3Department of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaDepartment of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaDepartment of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaDepartment of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaIn this study, a Back Propagation (BP) neural network algorithm based on Genetic Algorithm (GA) optimization is proposed to plan and optimize the trajectory of a redundant robotic arm for the upper limb rehabilitation of patients. The feasibility of the trajectory was verified by numerical simulations. First, the collected dataset was used to train the BP neural network optimized by the GA. Subsequently, the critical points designated by the rehabilitation physician for the upper limb rehabilitation were used as interpolation points for cubic B−spline interpolation to plan the motion trajectory. The GA optimized the planned trajectory with the goal of time minimization, and the feasibility of the optimized trajectory was analyzed with MATLAB simulations. The planned trajectory was smooth and continuous. There was no abrupt change in location or speed. Finally, simulations revealed that the optimized trajectory reduced the motion time and increased the motion speed between two adjacent critical points which improved the rehabilitation effect and can be applied to patients with different needs, which has high application value.https://www.mdpi.com/1424-8220/22/11/4071upper limb rehabilitation robotic armback propagation neural networkgenetic algorithmtrajectory planning |
| spellingShingle | Xiaohan Qie Cunfeng Kang Guanchen Zong Shujun Chen Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm Sensors upper limb rehabilitation robotic arm back propagation neural network genetic algorithm trajectory planning |
| title | Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm |
| title_full | Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm |
| title_fullStr | Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm |
| title_full_unstemmed | Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm |
| title_short | Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm |
| title_sort | trajectory planning and simulation study of redundant robotic arm for upper limb rehabilitation based on back propagation neural network and genetic algorithm |
| topic | upper limb rehabilitation robotic arm back propagation neural network genetic algorithm trajectory planning |
| url | https://www.mdpi.com/1424-8220/22/11/4071 |
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