| WANG Jinhong (王进洪)*,LEI Jingtao* **.[J].高技术通讯(英文),2022,28(4):411~417 |
|
| Workspace optimization of parallel robot by using multi-objective genetic algorithm |
| |
| DOI：10.3772/j.issn.1006-6748.2022.04.009 |
| 中文关键词: |
| 英文关键词: parallel robot, multi-objective genetic algorithm, workspace optimization |
| 基金项目: |
| Author Name | Affiliation | | WANG Jinhong (王进洪)* | (*School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China)
(** Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai 200444, P.R.China) | | LEI Jingtao* ** | (*School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China)
(** Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai 200444, P.R.China) |
|
| Hits: 420 |
| Download times: 479 |
| 中文摘要: |
| |
| 英文摘要: |
| For the narrow workspace problem of the universal-prismatic universal (UPU) parallel robot with fixed orientation, a kind of multi-objective genetic algorithm is studied to optimize the robot’s workspace. The concept of the effective workspace and its solution method are given. The effective workspace height (EWH) and global condition number index (GCI) of Jacobi matrix are selected as the optimized objective functions. Setting the robot in two different orientations, the geometric parameters are optimized by the multi-objective genetic algorithm named non-dominated sorting genetic algorithm II (NSGA-II), and a set of structural parameters is obtained. The optimization results are verified by four indicators with the robot’s moving platform at different orientations. The results show that, after optimization, the fixed-orientation workspace volume, the effective workspace height and the effective workspace volume increase by 32.4%, 17.8% and 72.9% on average, respectively. GCI decreases by 6.8% on average. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
