机器人工作空间求解的蒙特卡洛法改进和体积求取

徐振邦; 赵智远; 贺帅; 何俊培; 吴清文

doi:10.3788/OPE.20182611.2703

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机器人工作空间求解的蒙特卡洛法改进和体积求取

微纳技术与精密机械 | 更新时间：2020-07-05

- 机器人工作空间求解的蒙特卡洛法改进和体积求取
- Improvement of Monte Carlo method for robot workspace solution and volume calculation
- 光学精密工程 2018年26卷第11期页码：2703-2713
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所空间机器人工程中心空间机器人系统创新研究室, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
  
  [ "徐振邦(1982-), 男, 内蒙古通辽人, 研究员, 博士生导师, 2010年于中国科学技术大学获得博士学位, 主要从事空间智能机器人、空间遥感器设计、振动控制方面的研究。E-mail:xuzhenbang@gmail.com" ]
  [ "赵智远(1994-), 男, 甘肃岷县人, 硕士研究生, 2016年于吉林大学获得学士学位, 主要从事柔体机器人运动学的研究。E-mail:zhaozyjldx@126.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(11672290);吉林省科技发展计划资助项目(20160520074JH);中国科学院青年创新促进会资助项目(2014195)
- DOI：10.3788/OPE.20182611.2703
  中图分类号： TP242
- 收稿日期：2018-02-09，
  
  录用日期：2018-4-2，
  
  纸质出版日期：2018-11-25
- 稿件说明：
移动端阅览
徐振邦, 赵智远, 贺帅, 等. 机器人工作空间求解的蒙特卡洛法改进和体积求取[J]. 光学精密工程, 2018,26(11):2703-2713.

Zhen-bang XU, Zhi-yuan ZHAO, Shuai HE, et al. Improvement of Monte Carlo method for robot workspace solution and volume calculation[J]. Optics and precision engineering, 2018, 26(11): 2703-2713.
徐振邦, 赵智远, 贺帅, 等. 机器人工作空间求解的蒙特卡洛法改进和体积求取[J]. 光学精密工程, 2018,26(11):2703-2713. DOI： 10.3788/OPE.20182611.2703.

Zhen-bang XU, Zhi-yuan ZHAO, Shuai HE, et al. Improvement of Monte Carlo method for robot workspace solution and volume calculation[J]. Optics and precision engineering, 2018, 26(11): 2703-2713. DOI： 10.3788/OPE.20182611.2703.

摘要

针对传统的蒙特卡洛法求解机器人工作空间时精确度不够的问题，提出了一种改进的蒙特卡洛法。用传统的蒙特卡洛法生成一个种子工作空间，基于标准差动态可调的正态分布对种子工作空间进行扩展。在扩展过程中设定一个精度阈值，确保得到的工作空间中每个位置都能被准确的描述。基于得到的工作空间，提出了一种体元化算法求取工作空间的体积，寻找到工作空间的边界部分和非边界部分，通过对边界部分的不断细化，降低了体积求取误差。为了验证算法的有效性和实用性，以九自由度的超冗余串联机械臂为例，对本文改进的蒙特卡洛法和提出的体积求取算法进行仿真分析。结果表明：采样点数量相同时，改进的蒙特卡洛法生成的工作空间边界光滑，“噪声小”；得到精确的工作空间时改进方法需要的采样点数仅是传统方法的4.67%；体积求取算法效率较高，相对误差小于1%；求得的工作空间体积可用于评估机械臂性能，为后续机械臂构型优化奠定了理论基础。

Abstract

This study proposes an improved Monte Carlo method

considering that the traditional method lacks precision while calculating the workspace of a robot. The improved Monte Carlo method comprises two stages. In the first stage

a seed workspace is generated using the traditional Monte Carlo method. In the second stage

the seed workspace is expanded based on the normal distribution

and each region in the obtained workspace can be accurately described by setting an accuracy threshold in the process of expansion. Taking into account the characteristics of the normal distribution

to improve the efficiency of the expansion

dynamically adjustable standard deviations are used. Based on the obtained workspace

a voxel algorithm is proposed to determine the volume of the workspace. The algorithm for searching the boundary has been designed to locate the boundary as well as the non-boundary of the workspace. Refining the boundary alone reduces the calculation time and the resulting error. In order to verify the validity and practicability of the algorithm

the improved Monte Carlo method and the proposed volumetric algorithm were simulated and analyzed using a 9-degrees-of-freedom super-redundant serial robot. The results show that when the number of sampling points is the same

the boundary of the workspace generated by the improved Monte Carlo method is smoother and the noise is smaller. When the accurate workspace is obtained

the number of sampling points needed by the improved method is only 4.67% that of the traditional method. The designed volumetric algorithm is also more efficient

with a relative error less than 1%. The volume of workspace thus obtained can be used to evaluate the performance of a serial robot

which lays a theoretical foundation for the subsequent optimization of serial robot configuration.

关键词

Keywords

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