钢结构焊接机器人技术发展现状与趋势

李帅; 周绪红; 刘界鹏

doi:10.13206/j.gjgS26082945

钢结构焊接机器人技术发展现状与趋势

doi: 10.13206/j.gjgS26082945

李帅^1,2,
周绪红^1,2,
刘界鹏^1,2, ,

1. 重庆大学土木工程学院, 重庆 400045;
2. 重庆大学山地城镇建设与新技术教育部重点实验室, 重庆 400045

基金项目:

国家自然科学基金重点项目（52130801）。

详细信息

作者简介:
李帅，博士，博士后，主要从事焊接机器人、建筑机器人、智能建造的研究。

通讯作者:
刘界鹏，博士，教授，主要从事钢-混凝土混合结构、装配式混合结构建筑与桥梁、工程结构智能设计、建筑与桥梁智能化制造及智能建造的研究，liujp@cqu.edu.cn。

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出版历程
- 收稿日期: 2026-08-29

Development Status and Trends of Welding Robot Technology for Steel Structures

Li Shuai^1,2,
Zhou Xuhong^1,2,
Liu Jiepeng^{1,2
, ,}

1. School of Civil Engineering, Chongqing University, Chongqing 400045, China;
2. Key Laboratory of Mountain Cities Construction and New Technology, Ministry of Education, Chongqing University, Chongqing 400045, China

摘要

摘要: 钢结构制造与安装具有构件尺度大、非标准化程度高、小批量多品种、装配偏差与焊接热变形较为突出、厚板多层多道焊缝比例高，以及工厂制造与现场安装条件差异较大等特点，而汽车等重复制造行业中成熟的示教再现型焊接机器人难以直接移植到钢结构行业。为明确钢结构焊接机器人所处技术阶段、适用场景及发展方向，按照技术演进、工程应用、关键支撑技术、制约因素和发展趋势的逻辑顺序，对钢桥板单元制造、建筑钢结构工厂制造、复杂厚板节点焊接和现场安装等代表性场景中的机器人应用现状进行梳理，并综合分析近年来数字模型驱动、三维视觉、点云识别、多层多道自适应焊接、数字孪生、移动与协作机器人等的技术进展。结果表明：钢桥U肋板单元、横隔板等规则且重复度较高的工厂制造场景已形成相对成熟的专用装备与机器人自动化体系；H型、箱型及加劲构件等建筑钢结构工厂焊接正由离线编程向建筑信息模型（BIM）与计算机辅助设计（CAD）模型驱动、三维视觉识别和免示教作业方向发展；复杂非标节点、厚板多层多道焊以及高空、狭窄空间等现场作业仍是机器人规模化应用的主要难点。当前技术关注重点已由单纯提高机器人重复定位精度，扩展到对构件实际状态、坡口变化、热变形和焊缝成形的在线感知，以及焊接顺序、焊道排布、工艺参数和质量的闭环决策等方面。钢结构焊接机器人技术路径正在从依赖构件标准化以降低机器人作业难度，逐步拓展至依靠感知、规划与自适应能力以提高对非标准钢结构的适应性方向，且这两类路径在较长时期内仍将并存。未来研究和应用将更多关注模型、数据与焊接知识融合，多模态质量感知，自主移动化、人机协作化与多机器人并行，以及大模型和具身智能支持的任务理解、知识调用与持续学习；但在未来发展过程中，焊接工艺约束、质量验证、数据标准与安全认证仍是自主焊接工程应用的基本边界。
- 钢结构 /
- 焊接机器人 /
- 智能焊接 /
- 免示教 /
- 自主焊接 /
- 具身智能
Abstract: Steel structure fabrication and erection are characterized by large component dimensions， a high degree of non-standardization， small-batch and high-variety production， pronounced assembly deviations and welding-induced thermal deformation， a high proportion of multi-layer and multi-pass welds in thick plates， and substantial differences between factory fabrication and on-site erection conditions. Consequently， teach-and-playback welding robots， which have been widely adopted in repetitive manufacturing industries such as the automotive industry， cannot be directly transferred to steel structure applications. To clarify the current technological stage， applicable scenarios， and future development directions of welding robots for steel structures， this paper reviews representative application scenarios， including steel bridge panel units， factory fabrication of building steel structures， complex thick-plate joints， and on-site erection， following the logical sequence of technological evolution， engineering applications， key enabling technologies， constraints， and development trends. Recent advances in digital model-driven methods， three-dimensional vision， point cloud recognition， adaptive multi-layer and multi-pass welding， digital twins， and mobile and collaborative robots are also comprehensively analyzed. The review shows that relatively mature dedicated equipment and robotic automation systems have been developed for regular and highly repetitive factory fabrication scenarios， such as steel bridge U-rib panel units and diaphragms. Welding of H-shaped， box-shaped， and stiffened members in building steel structure fabrication is evolving from offline programming toward BIM- and CAD-model-driven operation， three-dimensional vision-based recognition， and teaching-free welding. However， complex non-standard joints， thick-plate multi-layer and multi-pass welding， and on-site operations at height or in confined spaces remain major challenges to the large-scale deployment of welding robots. The current technological focus has expanded from simply improving robotic repeatability to online perception of the actual component state， groove variations， thermal deformation， and weld-bead formation， as well as closed-loop decision-making for welding sequence， bead arrangement， process parameters， and weld quality. The technological pathways for robotic welding of steel structures are shifting from relying primarily on component standardization to reduce operational complexity toward enhancing adaptability to non-standard steel structures through improved perception， planning， and adaptive capabilities， while the two pathways are expected to coexist for an extended period. Future research and applications are likely to place greater emphasis on the integration of models， data， and welding knowledge； multimodal quality perception； autonomous mobility， human–robot collaboration， and multi-robot parallel operation； and task understanding， knowledge retrieval， and continual learning enabled by large-scale AI models and embodied intelligence. Nevertheless， welding process constraints， quality verification， data standards， and safety certification will remain fundamental boundaries for the engineering applications of autonomous welding systems.
- steel structure /
- welding robot /
- intelligent welding /
- teaching-free welding /
- autonomous welding /
- embodied intelligence

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参考文献(26)

[1]	戴为志，俞海涛，王继文. 钢结构焊接机器人的现状及发展方向[J]. 金属加工（热加工），2021（12）:7-12.
[2]	余宏亮，彭震，何梓睿. 基于多层次灰色理论的建筑钢结构焊接机器人技术应用成熟度评价[J]. 土木工程与管理学报，2023，40（3）:46-56.
[3]	吉敏廷. 箱型钢桥梁板单元自动化焊接技术应用研究[J]. 钢结构，2014，29（4）:54-57.
[4]	张华，宋神友，阮家顺，等. 机器人焊接技术在钢桥制造中的应用进展[J]. 金属加工（热加工），2021（12）:1-6.
[5]	朱志明，马国锐，刘晗，等. 箱型钢结构轨道式焊接机器人D-H模型与连续轨迹规划[J]. 焊接学报，2017，38（12）:95-98.
[6]	赵永庆，王春林，丁延胜，等. 智能化焊接机器人在高层钢结构制造和安装施工中的应用进展[J]. 城市建筑，2021，18（14）:124-126.
[7]	姚凯洵，王树强，陈光凯，等. 钢结构参数化设计与机器人焊接离线编程应用[J]. 机械工程与自动化，2024（2）:31-33.
[8]	李海周. 基于BIM的免示教焊接机器人在钢结构智能建造中的应用[J]. 施工技术（中英文），2023，52（5）:36-40.
[9]	Sarivan I M，Madsen O，Wæhrens B V. Automatic welding-robot programming based on product-process-resource models[J]. The International Journal of Advanced Manufacturing Technology，2024，132（3）:1931-1950.
[10]	郭小强，戴志彬. 基于BIM和机器视觉的钢结构智能焊接机器人系统分析[J]. 安装，2023（9）:70-72.
[11]	杨高阳，汪晓阳，孙朋，等. 大型钢结构机器人智能焊接技术及应用[J]. 焊接技术，2024，53（5）:94-96.
[12]	刘升华，马学亮，陆聪慧. 基于点云的钢结构焊缝识别系统研究[J]. 山西大同大学学报（自然科学版），2025，41（3）:115-121.
[13]	钟凡. 基于视觉特征的机车钢结构焊接机器人焊缝跟踪方法研究[J]. 电焊机，2024，54（9）:69-76.
[14]	Ma Y，Fan J，Hou J，et al. Vision-based autonomous robotic arc welding:state-of-the-art review and perspectives[J]. IEEE Transactions on Automation Science and Engineering，2025，22:21651-21673.
[15]	Hu Q，Wu X，Yang M，et al. A novel weld adaptive path generation method based on 3-D point cloud for robotic multilayer multipass welding[J]. IEEE Transactions on Instrumentation and Measurement，2025，74:1-13.
[16]	刘琦，文江涛，李步遥，等. 模型驱动的钢结构多层多道焊接机器人作业系统[J]. 施工技术（中英文），2026，55（13）:12-18.
[17]	Zhang Y M，Wang Q Y，Liu Y K. Adaptive intelligent welding manufacturing[J]. Welding Journal，2021，100（2）:63-83.
[18]	Khan A，Hussain S，Jawad M，et al. A real-time closed-loop control system for adaptive robotic welding:a step toward intelligent welding[J]. The International Journal of Advanced Manufacturing Technology，2025，141（9）:5223-5245.
[19]	张华，张清华，宋神友，等. 正交异性钢桥面板制造技术发展现状综述[J]. 钢结构（中英文），2022，37（4）:1-13.
[20]	高良，俞海涛，张伟. 建筑钢结构焊接机器人应用技术研究[J]. 焊接技术，2021，50（6）:74-77.
[21]	范军旗，王楠，徐向军. 钢桥现场自动化焊接技术应用分析与展望[J]. 焊接技术，2025，54（12）:1-3.
[22]	许子凡，孙蕾蕾，穆长春，等. 协作机器人在钢结构桥梁焊接中的应用[J]. 焊接技术，2026，55（1）:78-82.
[23]	Yao S，Xue L，Huang J，et al. Mobile welding robots under special working conditions:a review[J]. The International Journal of Advanced Manufacturing Technology，2025，138（9）:3907-3923.
[24]	Skriko T，Ahola A，Björk T. Overview on the digitized production of welded steel structures[J]. Welding in the World，2022，66（4）:799-813.
[25]	Lund H，Penttilä S，Skriko T. Advancing smart multi-robot welding through pre-setting and digital twin-based feedback to mitigate angular welding distortion[J]. Discover Mechanical Engineering，2025，4（1）:35.
[26]	Zitkovich B，Yu T，Xu S，et al. RT-2:vision-language-action models transfer web knowledge to robotic control[C]// Proceedings of the 7th Conference on Robot Learning. Atlanta:Proceedings of Machine Learning Research，2023:2165-2183.