Random Target Localization for an Upper Limb Prosthesis
To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/5297043 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549150073290752 |
|---|---|
| author | Xinglei Zhang Binghui Fan Chuanjiang Wang Xiaolin Cheng Hongguang Feng Zhaohui Tian |
| author_facet | Xinglei Zhang Binghui Fan Chuanjiang Wang Xiaolin Cheng Hongguang Feng Zhaohui Tian |
| author_sort | Xinglei Zhang |
| collection | DOAJ |
| description | To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5-DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm. |
| format | Article |
| id | doaj-art-2536e4c43d8a4a11b490ab66010cb60f |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-2536e4c43d8a4a11b490ab66010cb60f2025-02-03T06:12:00ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/52970435297043Random Target Localization for an Upper Limb ProsthesisXinglei Zhang0Binghui Fan1Chuanjiang Wang2Xiaolin Cheng3Hongguang Feng4Zhaohui Tian5College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical Engineering, Shandong University, Jinan 250100, ChinaCollege of Mechanical Engineering, Shandong University, Jinan 250100, ChinaCollege of Mechanical Engineering, Shandong University, Jinan 250100, ChinaTo achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5-DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.http://dx.doi.org/10.1155/2021/5297043 |
| spellingShingle | Xinglei Zhang Binghui Fan Chuanjiang Wang Xiaolin Cheng Hongguang Feng Zhaohui Tian Random Target Localization for an Upper Limb Prosthesis Shock and Vibration |
| title | Random Target Localization for an Upper Limb Prosthesis |
| title_full | Random Target Localization for an Upper Limb Prosthesis |
| title_fullStr | Random Target Localization for an Upper Limb Prosthesis |
| title_full_unstemmed | Random Target Localization for an Upper Limb Prosthesis |
| title_short | Random Target Localization for an Upper Limb Prosthesis |
| title_sort | random target localization for an upper limb prosthesis |
| url | http://dx.doi.org/10.1155/2021/5297043 |
| work_keys_str_mv | AT xingleizhang randomtargetlocalizationforanupperlimbprosthesis AT binghuifan randomtargetlocalizationforanupperlimbprosthesis AT chuanjiangwang randomtargetlocalizationforanupperlimbprosthesis AT xiaolincheng randomtargetlocalizationforanupperlimbprosthesis AT hongguangfeng randomtargetlocalizationforanupperlimbprosthesis AT zhaohuitian randomtargetlocalizationforanupperlimbprosthesis |