Accurate error compensation method for multi-axis parallel machine via singularized jacobi geometric parameter correction and coupling error evaluation

雅可比矩阵与行列式 残余物 补偿(心理学) 联轴节(管道) 计算机科学 工作区 算法 控制理论(社会学) 数学 应用数学 人工智能 工程类 机械工程 控制(管理) 精神分析 心理学 机器人
作者
Yuheng Luo,Jian Gao,Disai Chen,Lanyu Zhang,Yachao Liu,Yongbin Zhong
出处
期刊:Robotics and Computer-integrated Manufacturing [Elsevier]
卷期号:89: 102771-102771 被引量:2
标识
DOI:10.1016/j.rcim.2024.102771
摘要

The Jacobian model is a prevalent tool for error compensation in multi-axis parallel mechanisms. However, discrepancies between the model's nominal and actual geometrical parameters, combined with equivalent replacements and high-order rounding in the modeling process, lead to equation solving challenges and modeling errors. These inaccuracies result in residual errors in the Jacobian model compensation. To address these problems, this paper proposes an optimal Jacobian correction approach. This is based on a geometrical parameter singularized Jacobian correction model, and a module for the evaluation of coupling errors for multi-axis parallel mechanisms was incorporated. Instead of relying on iterative processes, a singularized geometrical error solution method (SESM) was developed. Through this method, precise derivation of the Jacobian correction parameters is ensured, effectively addressing the indefinite equation challenge and partial posture non-solution problem. Moreover, modeling errors resulting from equivalent infinitesimal replacements and the overlooking of high-order minor values are compensated for by the SESM. It was observed that varying singularized geometrical parameters in the Jacobian model can produce different coupling effects and compensation outcomes. Therefore, a sensitivity-based error predictive evaluation method (EPEM) was introduced. By this method, the optimal correction parameter of the Jacobian model across the entire workspace is identified, ensuring precise pose error compensation. The proposed method was validated using a three-axis parallel mechanism. Through these tests, its superior efficacy was revealed. In comparison to the traditional uncorrected Jacobian compensation, reductions in position and orientation errors by 64.93% and 55.29%, respectively, were achieved. This method provides a new approach for error modeling, equation solving, and parameter correction for multi-axis mechanism error compensation and precision equipment development.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
2秒前
扶摇完成签到 ,获得积分10
3秒前
bopop发布了新的文献求助10
3秒前
剑K完成签到,获得积分10
3秒前
Shirley发布了新的文献求助10
3秒前
潇潇雨歇发布了新的文献求助200
4秒前
量子星尘发布了新的文献求助10
7秒前
juzi发布了新的文献求助20
7秒前
JamesPei应助Betty采纳,获得10
7秒前
fang完成签到,获得积分10
7秒前
dreamvssnow完成签到 ,获得积分10
8秒前
9秒前
10秒前
庚桑楚完成签到,获得积分10
13秒前
嘻嘻完成签到 ,获得积分10
13秒前
量子星尘发布了新的文献求助10
14秒前
14秒前
14秒前
随随完成签到 ,获得积分10
14秒前
bopop完成签到,获得积分10
15秒前
眯眯眼的代容完成签到,获得积分10
15秒前
韩楠完成签到 ,获得积分10
15秒前
NexusExplorer应助天道酬勤采纳,获得30
16秒前
17秒前
兰是一个信仰完成签到,获得积分10
19秒前
orixero应助Vanessa采纳,获得10
20秒前
21秒前
三金完成签到,获得积分20
21秒前
健忘洋葱完成签到 ,获得积分10
22秒前
精明凡双完成签到,获得积分0
22秒前
1212发布了新的文献求助10
23秒前
多情雨灵发布了新的文献求助80
24秒前
三金发布了新的文献求助30
25秒前
科研通AI2S应助寒江雪采纳,获得10
25秒前
含糊的曼香完成签到 ,获得积分10
25秒前
今后应助YANG采纳,获得10
26秒前
无情的问枫完成签到 ,获得积分10
26秒前
27秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Introduction to strong mixing conditions volume 1-3 5000
Ägyptische Geschichte der 21.–30. Dynastie 2500
Human Embryology and Developmental Biology 7th Edition 2000
The Developing Human: Clinically Oriented Embryology 12th Edition 2000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
„Semitische Wissenschaften“? 1510
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5742127
求助须知:如何正确求助?哪些是违规求助? 5406259
关于积分的说明 15344129
捐赠科研通 4883566
什么是DOI,文献DOI怎么找? 2625108
邀请新用户注册赠送积分活动 1573970
关于科研通互助平台的介绍 1530929