面向机器人螺栓装配的视觉感知与力控协同方法

张春云; 孟昕曈; 陶陶; 周怀东

doi:10.11999/JEIT251193

面向机器人螺栓装配的视觉感知与力控协同方法

doi: 10.11999/JEIT251193 cstr: 32379.14.JEIT251193

1.
安徽工业大学计算机科学与技术学院马鞍山 243032
2.
北京信息科技大学自动化学院北京 100192
3.
清华大学计算机科学与技术系北京 100084

基金项目: 国家自然科学基金联合资助项目(U22A2057, U22B2042)

详细信息

作者简介:
张春云：男，硕士生，研究方向为视觉伺服、机器人运动控制、灵巧操作

孟昕曈：女，硕士生，研究方向为图像处理、目标检测、机械臂控制

陶陶：男，教授，研究方向为智能物联网、嵌入式系统、数据隐私保护

周怀东：男，副研究员，研究方向为视触感控一体化灵巧手智能操作、智能体类人操作技能知识化表达

通讯作者:
周怀东　hdzhou@tsinghua.edu.cn

中图分类号: TP249
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- 被引次数: 0
出版历程
- 收稿日期: 2025-11-13
- 修回日期: 2026-01-22
- 录用日期: 2026-01-22
- 网络出版日期: 2026-02-01

Vision-Guided and Force-Controlled Method for Robotic Screw Assembly

1.
School of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243032, China
2.
School of Automation, Beijing University of Information Science and Technology, Beijing 100192, China
3.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Funds: The Joint Funds of the National Natural Science Foundation of China (U22A2057, U22B2042)

摘要

摘要: 随着工业自动化与智能制造的发展，机器人在精密装配任务中应用广泛，尤其在螺栓装配等高精度作业环节中发挥着重要作用。然而，在螺栓装配过程中，存在目标物体位姿不确定、微小孔位识别困难以及末端执行器姿态缺乏动态闭环修正等问题。为此，该文提出一种面向机器人螺栓装配的视觉感知与力控协同方法。首先，构建语义增强的6D位姿估计算法，通过融合开放词汇目标检测模块与通用分割模块增强目标感知能力，提升初始位姿精度，并在连续帧跟踪中引入语义约束与平移修正，实现动态环境下稳健跟踪。其次，设计基于改进NanoDet的螺纹孔检测算法，采用轻量级MobileNetV3作为特征提取网络，并增加圆形分支检测头，有效提高微小孔位的识别精度与边界拟合能力，为后续装配提供可靠特征基础。最后，提出分层视觉引导与力控协同的装配策略，通过全局粗定位与局部精定位逐级优化目标位姿，并结合末端力觉反馈进行姿态微调，实现螺栓与螺纹孔的高精度对准与稳定装配。实验结果表明，该文方法在装配精度、鲁棒性及稳定性方面均具有显著优势，具备良好的工程应用前景。
- 机器人螺栓装配 /
- 视觉-力觉协同 /
- 6D位姿估计 /
- 螺纹孔检测 /
- 顺应控制
Abstract: Objective With the rapid development of intelligent manufacturing and industrial automation, robots are increasingly applied to high-precision assembly tasks, especially screw assembly. However, current systems still face several challenges. The pose of assembly objects is often uncertain, which makes initial localization difficult. Small features such as threaded holes are blurred and difficult to identify accurately. Conventional vision-based open-loop control may also cause assembly deviation or jamming. This study proposes a vision–force cooperative method for robotic screw assembly. The method establishes a closed-loop assembly system that covers coarse positioning and fine alignment. A semantic-enhanced 6D pose estimation algorithm and a lightweight hole detection model are used to improve perception accuracy. Force-feedback control then adjusts the end-effector posture dynamically. This approach improves the accuracy and stability of screw assembly. Methods The proposed screw-assembly method is based on a vision–force cooperative strategy that forms a closed-loop process. In the visual perception stage, a semantic-enhanced 6D pose estimation algorithm addresses disturbances and pose uncertainty in complex industrial environments. During initial pose estimation, Grounding DINO and SAM2 generate pixel-level masks that provide semantic priors for the FoundationPose module. In the continuous tracking stage, semantic cues from Grounding DINO support translational correction. To detect small threaded holes, an improved lightweight hole detection algorithm based on NanoDet is designed. It uses MobileNetV3 as the backbone and adds a CircleRefine module in the detection head to estimate hole centers precisely. In the assembly positioning stage, a hierarchical vision-guided strategy is used. The global camera performs coarse positioning for overall guidance, while the hand–eye camera conducts local correction using hole detection results. In the closed-loop assembly stage, force-feedback control adjusts the posture to achieve accurate alignment between the screw and the threaded hole. Results and Discussions The method is validated experimentally in robotic screw assembly scenarios. The improved 6D pose estimation algorithm reduces the average position error by 18% and the orientation error by 11.7% compared with the baseline (Table 1). The tracking success rate in dynamic sequences increases from 72% to 85% (Table 2). For threaded hole detection, the lightweight NanoDet-based algorithm is evaluated on a dataset collected from assembly environments. It achieves 98.3% precision, 99.2% recall, and 98.7% mAP (Table 3). The model size is 11.7 MB and the computational cost is 2.9 GFLOPs, which are both lower than most benchmark models while maintaining high accuracy. A circular branch is introduced to fit hole edges (Fig. 8), providing accurate center predictions for visual guidance. Under different inclination angles (Fig. 10), the assembly success rate remains above 91.6% (Table 4). For screws of different sizes (M4, M6, and M8), the success rate remains above 90% (Table 5). Under small external disturbances (Fig. 12), the success rates reach 93.3%, 90%, and 83.3% for translational, rotational, and mixed disturbances, respectively (Table 6). Force-feedback comparison experiments show that the success rate is 66.7% under visual guidance alone. With force-feedback control, the rate increases to 96.7% (Table 7). The system maintains stable performance throughout complete screw-assembly cycles and achieves an average cycle time of 9.53 s (Table 8), meeting industrial assembly requirements. Conclusions This study presents a vision–force cooperative method that addresses key challenges in robotic screw assembly. The approach enhances target localization accuracy through a semantic-enhanced 6D pose estimation algorithm and a lightweight threaded hole detection network. The integration of hierarchical vision guidance and force-feedback control enables precise alignment between screws and threaded holes. Experimental results show that the method ensures reliable assembly under varied conditions, providing a practical solution for intelligent robotic assembly. Future work will focus on adaptive force control, multimodal perception fusion, and intelligent task planning to further improve generalization and self-optimization in complex industrial environments.
- Robotic screw assembly /
- Vision–force coordination /
- 6D pose estimation /
- Threaded hole detection /
- Compliance control

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图 1 螺栓装配流程示意图

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图 2 6D位姿估计算法框架图

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图 3 改进NanoDet模型结构图

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图 4 末端姿态修正示意图

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图 5 实验平台整体配置

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图 6 动态场景下部分跟踪结果对比

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图 7 不同算法孔位检测结果对比图

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图 8 圆孔拟合结果

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图 9 螺栓装配实验台

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图 10 不同倾角装配实验图

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图 11 不同规格螺栓装配实验图

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图 12 不同扰动条件下装配实验图

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图 13 螺栓装配过程中力觉变化对比

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表 1 初始位姿估计对比实验结果

任务	FoundationPose		本文方法
任务	PE(m)	OE(°)	PE(m)	OE
1	0.0121	5.083	0.0095	3.974
2	0.0120	5.122	0.0091	4.439
3	0.0101	4.281	0.0083	4.006
4	0.0106	3.462	0.0097	3.158
5	0.0121	4.228	0.0098	3.969
6	0.0120	4.047	0.0083	3.953
7	0.0102	3.796	0.0920	3.411
8	0.0111	4.821	0.0102	3.724
9	0.0106	5.108	0.0088	4.501
10	0.0103	4.656	0.0081	4.204
平均误差	0.0111	4.460	0.0091	3.934

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表 2 跟踪性能对比实验结果

算法		总帧数	成功帧数	TSR(%)
FoundationPose	视频1	641	502	72
	视频2	917	698
	视频3	695	428
本文方法	视频1	641	576	85
	视频2	917	797
	视频3	695	548

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表 3 不同模型的对比实验结果

模型	mAP@0.5(%)	P(%)	R(%)	Weights(MB)	GFLOPs
RetinaNet	26.3	98.1	26.6	145.7	34.6
YOLOv7	97.4	96.4	97.2	74.8	13.2
YOLOv8	98.1	98.5	91.4	18.9	6.0
YOLOv11	98.1	98.6	92.8	15.6	4.6
NanoDet	97.5	96.7	98.8	7.6	1.9
本文方法	98.7	98.3	99.2	11.7	2.9

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表 4 不同倾角装配性能实验结果

倾角(°)	实验次数	成功次数	成功率(%)
0	30	29	96.7
15	30	29	96.7
30	30	27	90.0
45	30	25	83.3

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表 5 不同螺栓规格装配性能实验结果

螺栓规格	方法(原NanoDet)			本文方法(改进NanoDet)
螺栓规格	实验次数	成功次数	成功率(%)	实验次数	成功次数	成功率(%)
M8	30	28	93.3	30	30	100
M6	30	27	90	30	29	96.7
M4	30	24	80	30	27	90

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表 6 动态扰动装配性能实验结果

扰动类型	实验次数	成功次数	成功率(%)
平移扰动	30	28	93.3
旋转扰动	30	27	90.0
混合扰动	30	25	83.3

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表 7 力觉反馈对比实验结果

实验条件	实验次数	成功次数	成功率(%)
仅视觉	30	20	66.7
本文方法	30	29	96.7

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表 8 全流程装配各阶段时间统计结果

装配环节	平均耗时(s)	标准差(s)
初始位姿估计	3.600	0.250
粗定位移动	1.200	0.180
螺纹孔检测	0.086	0.009
精定位移动	0.970	0.080
姿态微调	2.150	0.220
螺栓锁付	1.520	0.100
单次装配总周期时间	9.530	0.420

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