- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | DOU Rutong, YU Shenbo, SUN Feng, XIA Pengpeng, YOKOI Hiroshi, JIANG Yinlai. Density-Reducing Monte Carlo Method for 7 Degrees of Freedom Humanoid Robot Arm Workspace Solution[J]. Journal of Southwest Jiaotong University, 2023, 58(6): 1328-1338. doi: 10.3969/j.issn.0258-2724.20220777
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A density-reducing Monte Carlo method was proposed to address the problems of inaccurate precision and waste of encrypted point cloud in the Monte Carlo method and the improved Monte Carlo method for solving robot arm workspace. Firstly, based on the characteristic of uneven distribution of random points in the Monte Carlo method, the initial workspace of the robot arm was uniformly densified to make the inner and boundary regions of the space clear. Then, only the boundary region was encrypted by adopting the extended joint angle and the cyclic encryption of random points, so as to reduce the density of the random point cloud in the workspace. Meanwhile, the influence of initial point cloud quantity, axial segmentation voxel quantity, precision threshold, extended joint angle, and cycle number on the precision of the workspace was studied. Finally, the effectiveness of the density-reducing Monte Carlo method was verified by simulation analysis. The results show that compared with the Monte Carlo method, the total number of random point clouds of the density-reducing Monte Carlo method decreases by 93.89% when the average error rate of the workspace is 0.022 42%. In addition, compared with the improved Monte Carlo method, the density-reducing Monte Carlo method reduces the average error rate of the workspace by 0.138 53% and 0.113 29% when the number of cycles is 2 and 4, and the total number of random point clouds decreases by 44.83% and 64.52%.
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