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浙江大学学报(工学版)
浙江大学学报(工学版)  2025, Vol. 59 Issue (11): 2229-2236    DOI: 10.3785/j.issn.1008-973X.2025.11.001
机械工程、能源工程     
考虑多源混合不确定性的并联机器人降敏设计
陶明哲1,2(),徐敬华1,*(),张树有1,谭建荣1
1. 浙江大学 设计工程研究所，浙江 杭州 310058
2. 经血管植入器械全国重点实验室，浙江 杭州 310009
Desensitization design for parallel robots under multi-source hybrid uncertainty
Mingzhe TAO1,2(),Jinghua XU1,*(),Shuyou ZHANG1,Jianrong TAN1
1. Design Engineering Institute, Zhejiang University, Hangzhou 310058, China
2. State Key Laboratory of Transvascular Implantation Devices and TRIDI, Hangzhou 310009, China
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摘要：

针对高性能并联机器人优化设计的难题，提出考虑多源不确定混合扰动的并联机器人降敏设计方法. 利用一阶摄动方法进行误差建模，建立概率误差模型. 在分析工作子区域所对应的高价值靶目标后，采用多靶分域元启发式迭代获取最优的尺寸设计参数. 构建性能敏感度指标，对机器人设计公差进行优化分配. 通过建立在役精度性能敏感性模型，计算获取维保效果对于预防性维保策略参数的敏感性，制定得到低敏预防性维保策略. 以增材制造并联机器人为例进行分析，结果表明，利用降敏设计方法，能够有效地提高静态设计性能及动态在役精度保持能力.
关键词： 并联机器人;  多源混合不确定性;  降敏设计;  多靶分域迭代;  公差优化分配;  预防性维保    
Abstract:

A desensitization design method for parallel robots considering multi-source uncertain hybrid perturbation was proposed aiming at the problem of optimal design of high-performance parallel robots. A probabilistic error model was established by using the first-order perturbation method for error modeling. The optimal dimension design parameters were obtained by using multi-target subregion meta-heuristic iterations after analyzing the high-value targets corresponding to the working subregion. A performance sensitivity index was constructed to optimally allocate the design tolerances. The sensitivity of maintenance to parameters was calculated by establishing an in-service accuracy performance sensitivity model, and a low-sensitivity preventive maintenance strategy was obtained. An additive manufacturing parallel robot was used as an example for validation. Results show that the static performance and dynamic in-service accuracy maintenance can be effectively improved via desensitization design.
Key words: parallel robots    multi-source hybrid uncertainty    desensitization design    multi-target subregion iteration    optimal tolerance allocation    preventive maintenance
收稿日期: 2024-10-25 出版日期: 2025-10-30
:  TP 391  
基金资助: 国家重点研发计划资助项目（2022YFB3303303）；国家科技重大专项资助项目（2024ZD0714401）
通讯作者: 徐敬华     E-mail: 12225017@zju.edu.cn;xujh@zju.edu.cn
作者简介: 陶明哲（1997—），男，博士生，从事并联机器人的研究. orcid.org/0000-0003-1568-4786. E-mail：12225017@zju.edu.cn
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陶明哲
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引用本文:

陶明哲,徐敬华,张树有,谭建荣. 考虑多源混合不确定性的并联机器人降敏设计[J]. 浙江大学学报(工学版), 2025, 59(11): 2229-2236.

Mingzhe TAO,Jinghua XU,Shuyou ZHANG,Jianrong TAN. Desensitization design for parallel robots under multi-source hybrid uncertainty. Journal of ZheJiang University (Engineering Science), 2025, 59(11): 2229-2236.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.11.001    或    https://www.zjujournals.com/eng/CN/Y2025/V59/I11/2229

图 1  面向增材制造的并联机器人机构简图
候选解设计变量/cm适应度
$ {r}_{{\mathrm{c}}} $$ {r}_{{\mathrm{a}}} $$ {l}_{{\mathrm{c}}} $$ {l}_{{\mathrm{a}}} $$\left| {{\mathrm{\Delta }{{\boldsymbol Z}}}} \right|/{\mathrm{mm}}$$ k $$ {F}_{\max} $$ {D}_{\max} $
110555115158.710.16281.321.09×10?41.60×104
296.1759.91115.02159.900.16581.101.01×10?41.20×104
3104.9955115148.130.16271.401.07×10?41.84×104
4104.9755115158.710.16281.321.09×10?41.60×104
5(选用解)86.8356.85115.43156.990.16681.07519.63×10?51.20×104
表 1  多靶分域优化后的并联机器人候选尺寸
方法设计变量$ /\mathrm{c}\mathrm{m} $适应度
$ {r}_{{\mathrm{c}}} $$ {r}_{{\mathrm{a}}} $$ {l}_{{\mathrm{c}}} $$ {l}_{{\mathrm{a}}} $$ \left\| \Delta{\boldsymbol{Z}} \right\| /{\mathrm{mm}}$$ k $$ {F}_{\max} $$ {D}_{\max} $
原始设计变量90651251500.16911.188.88×10?51.58×104
PSO105551151400.16271.461.06×10?42.02×104
NSGA-II87.8564.31115.02159.680.16961.141.05×10?41.24×104
多靶分域优化86.8356.85115.43156.990.16681.079.63×10?51.20×104
表 2  多靶分域优化与典型优化算法的效果对比
图 2  各独立误差源的误差敏感度
图 3  执行公差优化分配前、后的效果对比
编号基本尺寸/cm公差优化
分配值		依照国标
修整值		对应公差
等级
1120~18088.0 μm100 μmIT9
2120~18088.2 μm100 μmIT9
3120~18088.2 μm100 μmIT9
480~120134.0 μm140 μmIT10
580~120150.0 μm140 μmIT10
680~120150.0 μm140 μmIT10
7100~160622.28 μrad500 μradAT9
8100~160729.33 μrad800 μradAT10
9100~160729.33 μrad800 μradAT10
1040~631500 μrad1250 μradAT10
1140~631300 μrad1250 μradAT10
1240~631300 μrad1250 μradAT10
1363~1001200 μrad1000 μradAT10
1463~100500 μrad400 μradAT8
1563~100500 μrad400 μradAT8
1650~8050.0 μm46 μmIT8
1780~12050.0 μm54 μmIT8
表 3  修整后的公差等级优化分配
参数数值参数数值
$ \mu $0.000 3$ {t}_{{\mathrm{m}}}/{\mathrm{d}} $0.5 d
$ \sigma $0.000 1$ {N}_{{\mathrm{m}}} $10
$ {C}_{{\mathrm{m}}} $/元1 200$ {e}_{\varepsilon }/{\mathrm{mm}} $[0.1, 0.3]
$ {C}_{{\mathrm{b}}} $/元5 000——
表 4  误差源精度退化模型与在役精度性能敏感性模型的参数表
图 4  增材制造并联机器人预防性维保收益的计算结果
维保策略LR/d
未实施维保281
实施普通维保方法（Nm=0）1124
实施高敏维保策略参数的预防性维保方法（Nm=2）2036
实施高敏维保策略参数的预防性维保方法（Nm=8）1456
预防性维保降敏设计方法（Nm=4）2297
表 5  不同维保策略在增材制造并联机器人上的效果对比
图 5  止回阀沿轮廓的误差分布与增材制造的示意图
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