多机器人协同光学加工技术

石丰华; 李龙响; 刘夕铭; 彭利荣; 陈昊; 李兴昶; 程强; 张学军

doi:10.37188/CO.2025-0020

多机器人协同光学加工技术

doi: 10.37188/CO.2025-0020

cstr: 32171.14.CO.2025-0020

石丰华^{1, 2, 3,},
李龙响^{1, 2, 3, ,},
刘夕铭^{1, 2, 3},
彭利荣^{1, 2, 3},
陈昊^{1, 2, 3},
李兴昶^{1, 2, 3},
程强^{1, 2, 3},
张学军^{1, 2, 3}

1.
中国科学院长春光学精密机械与物理研究所
2.
中国科学院大学
3.
光学系统先进制造全国重点实验室

基金项目: 国家重点研发计划（No. 2022YFB3403405）；国家自然科学基金（No. 62275246）；国防科工局技术基础科研项目大口径光学反射镜××提升技术研究（No. JSZL 2023130B00）；省预算内基本建设资金-新兴产业自主创新能力提升工程（No. 202 4C001，No. 2022C047-3）

详细信息

作者简介:
石丰华（1999—），男，吉林通化人，硕士研究生，2021年于吉林大学获得学士学位，2022年至今为长春光机所硕士研究生，主要从事多机器人协同加工光学加工研究。E-mail：shifenghua22@mails.ucas.ac.cn

李龙响（1987—），男，安徽宿州人，博士，研究员，2011年于哈尔滨工业大学获得学士学位，2016年于中国科学院大学获得博士学位，现为中国科学院长春光机所光学中心副主任，主要从事磁流变光学加工和检测方面的研究工作。E-mail：leellxhit@126.com

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出版历程
- 网络出版日期: 2025-04-22

Multi-robot collaborative optical processing

SHI Feng-hua^{1, 2, 3
,},
LI Long-xiang^{1, 2, 3
, ,},
LIU Xi-ming^{1, 2, 3},
PENG Li-rong^{1, 2, 3},
CHEN Hao^{1, 2, 3},
LI Xing-chang^{1, 2, 3},
CHENG Qiang^{1, 2, 3},
ZHANG Xue-jun^{1, 2, 3}

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences
2.
University of Chinese Academy of Sciences
3.
State Key Laboratory of Advanced Manufacturing for Optical Systems

Funds: Supported by National Key Research and Development Program of China (No. 2022YFB3403405); National Natural Science Foundation of China (No. 62275246); Defense Industrial Technology Development Program (No. JSZL 2023130B00); capital construction funds (No. 202 4C001, No. 2022C047-3)

More Information

Corresponding author: leellxhit@126.com

摘要

摘要:
为了提升大口径光学元件的加工效率，提出了采用多个加工单元分别同时负载单个工具并共同执行加工任务，以降低加工单元执行时间从而大幅提升加工效率方法。首先，根据光学元件提出一种多机器人协同加工布局，接着，针对三种潜在的可行轨迹进行了模拟加工。然后，在离散仿真基础上，得出了轨迹参数的选取原则，针对离散仿真无法体现轨迹连续性对面形影响这一局限提出并建立了运动模式适配的积分去除函数模型，此模型将轨迹连续性的影响引入模拟加工中，最后提出一种协同加工避障策略并采用最优轨迹进行了协同加工的效率提升验证实验。实验结果表明：将初始面形PV=18.310λ（λ=632.8 nm），RMS为1.788λ的元件收敛至PV=4.873λ，RMS=1.113λ，与此同时其直径120 mm的有效区间内PV=4.661λ，RMS=0.857λ，经加工后收敛至PV=2.465λ，RMS=0.622λ。总执行时间为3.943h,若采用协同加工策略单个加工单元最长执行时间为2.041h，相比单工具效率提升1.93倍。与单机器人加工相比，该方法使得加工效率显著提高，面形精度得到保证，有效缩短了加工周期，在大孔径光学元件制造中具有很强的适用性和潜力，同时也为未来使用更多机器人的协同光学制造提供了轨迹规划策略参考和奠定了基础。
- 多机器人协同光学加工 /
- 光学超精密制造 /
- 积分去除函数 /
- 轨迹规划 /
- 高效光学制造.
Abstract:
To improve the processing efficiency of large-aperture optical components, a multi-robot, multi-tool collaborative processing method was proposed. A collaborative layout that has been tailored to the optical components was designed, and three feasible trajectories were simulated for analysis. The discrete simulation results were then used to establish principles for selecting trajectory parameters. To address the limitation of discrete simulation in capturing the influence of trajectory continuity on the surface map, an integral removal function model adapted to the motion mode was introduced. Furthermore, a collaborative machining obstacle avoidance strategy was developed. The experimental results obtained using the optimal trajectory demonstrated that with an initial surface shape of PV=18.310λ (λ=632.8 nm) and RMS=1.788λ, the final surface achieved PV=4.873λ and RMS=1.113λ. In addition, within the effective range of 120 mm diameter, PV=4.661 λ, RMS=0.857λ, converged to PV=2.465λ and RMS=0.622λ after processing. The total execution time was 3.943 hours, with the maximum execution time for a single processing unit being 2.041 hours, representing a 1.93-fold improvement over single-tool processing. This method significantly enhances processing efficiency, ensures surface shape accuracy, and holds great potential for the manufacturing of large-aperture optical components.
- multi-robot collaborative optical processing /
- optical ultra-precision manufacturing /
- continuous tool influence function /
- trajectory planning /
- efficient optical manufacturing

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图 1 多机器人协同加工布局

Figure 1. Multi-robot collaborative processing layout

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图 2 轨迹规划与区域间隔

Figure 2. Trajectory planning and regional spacing

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图 3 仿真流程图

Figure 3. Simulation flowchart

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图 4 光栅轨迹模拟加工结果

Figure 4. Simulation results of the raster trajectory

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图 5 梳形轨迹模拟加工结果

Figure 5. Simulation results of the comb trajectory

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图 6 组合轨迹模拟加工结果

Figure 6. Simulation results of the combined trajectory

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图 7 离散仿真原理图

Figure 7. Schematic diagram of discrete simulation

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图 8 运动模式适配的积分去除函数建模

Figure 8. CTIF (Continuous TIF) modeling for motion adaptation

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图 9 连续性模拟加工原理图

Figure 9. Schematic diagram of the continuity simulation

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图 10 连续性模拟加工结果与离散模拟加工结果对比图

Figure 10. Comparison of continuous simulation results and discrete simulation results

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图 11 避障策略

Figure 11. Obstacle avoidance strategies

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图 12 机器人加工单元与工具

Figure 12. Robotic processing unit and tool

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图 13 工件初始面形

Figure 13. Initial surface map of the workpiece

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图 14 有效范围内初始面形

Figure 14. Initial surface map of the effective area

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图 17 驻留时间分布

Figure 17. Dwell time distribution

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图 15 加工结果

Figure 15. Processing results

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图 16 有效范围内加工结果

Figure 16. Processing result of the effective area

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图 18 高通滤波结果

Figure 18. High-pass filtering results

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图 19 实际加工中高频误差

Figure 19. Mid-high frequency errors in actual processing

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图 20 模拟加工中高频误差

Figure 20. Simulation of mid-high-frequency errors in processing

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表 1 单机器人和多机器人协同加工耗时对比

Table 1. Comparison of the time consuming of single robot and multi-robot collaborative processing

轨迹种类	单机器人加工时长	协同加工消耗时长	协同加工耗时占比
光栅轨迹	3.562 h	1.788 h	50.20%
梳形轨迹	2.479 h	1.245 h	50.22%
组合轨迹	3.532 h	1.770 h	50.11%

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