融合协方差分析与随机采样拟合的轮胎空间位姿估计及机器人拆垛方法

doi:10.3901/JME.260324

机械工程学报 ›› 2026, Vol. 62 ›› Issue (15): 324-333.doi: 10.3901/JME.260324

• 机器人与机构学 • 上一篇

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融合协方差分析与随机采样拟合的轮胎空间位姿估计及机器人拆垛方法

王辉强¹, 李祥飞¹, 赵欢¹, 李楠², 袁惯喆¹, 丁汉¹

1. 华中科技大学智能制造装备与技术全国重点实验室武汉 430074;
2. 志研自动化设备湖北有限公司咸宁 437000

收稿日期:2025-08-17 修回日期:2025-11-25 发布日期:2026-10-13
作者简介:王辉强，男，2001年出生。主要研究方向为机器人测量覆盖路径规划。E-mail：M202470720@hust.edu.cn;李祥飞(通信作者)，男，1990年出生，助理研究员。主要研究方向为机器人灵巧协同装配。E-mail：lixiangfei@hust.edu.cn;赵欢，男，1983 年出生，博士，教授，博士研究生导师。主要研究方向为机器人智能化加工与装配技术。E-mail：huanzhao@hust.edu.cn;李楠，男，1993年出生，硕士。主要研究方向为计算机视觉。E-mail：929810135@qq.com;袁惯喆，男，2003年出生，本科生。主要研究方向为机器视觉。E-mail：u202110960@hust.edu.cn;丁汉，男，1963年出生，教授，博士研究生导师。主要研究方向为数字化制造与机器人技术。E-mail：dinghan@hust.edu.cn
基金资助:
湖北省科技厅科技服务人才专项(2024DJC084)和国家自然科学基金卓越研究群体(52188102)资助项目。

Tire Pose Estimation and Robotic Depalletizing Method Integrating Covariance Analysis and Random Sample Consensus

WANG Huiqiang¹, LI Xiangfei¹, ZHAO Huan¹, LI Nan², YUAN Guanzhe¹, DING Han¹

1. State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074;
2. Zhiyan Automatic Equipment Hubei Co., Ltd, Xianning 437000

Received:2025-08-17 Revised:2025-11-25 Published:2026-10-13

摘要/Abstract

摘要： 汽车行业轮胎总成拆垛工序中，工件负载重，工序时效性与稳定性要求严苛，同时由于工件摆放位置浮动，难以采用机器人示教方法进行高效自动化拆垛。现有主流的人工拆垛及桁架吊装助力设备方式自动化程度低；为满足时效性要求，人工拆垛方式劳动强度大、人力成本高，同时存在短期物料坠落风险及长期劳损安全隐患。为此，提出了一种使用工业机器人与面结构光相机对轮胎总成空间位姿识别及自动化拆垛方法。本方法针对轮胎总成及其料笼空间堆叠特征与拆垛工序要求，设计了点云测量及数据预处理过程，以确保其数据完整与后续计算精度，其次因轮胎总成表面非简单参数化模型外表，通过轮胎总成几何特点与工件点云协方差矩阵计算及法矢量滤波实现了点云边界修整，设计了目标工件空间位姿计算过程，以满足其工序自动化需求。最后，以工业机器人与面结构光相机为主体建立实验平台，利用本文所提测量及计算过程实现轮胎总成空间位姿计算。通过重复试验验证，结果表明所提出的轮胎总成空间位姿识别方法能够有效实现轮胎总成拆垛工序自动化，包括识别准确率100%，重复精度小于±5 mm，平均单层多个目标位姿计算时间小于1.2 s，测量、计算及机器人拆垛总工序用时小于13 s每工件。

关键词: 工业机器人, 面结构光相机, 拆垛自动化

Abstract: In the automotive industry, the depalletizing process for tire assemblies presents several challenges. The workpieces are heavy, and the process demands strict timing and stability requirements. Additionally, the floating positioning of the workpieces makes it difficult to achieve efficient automation through traditional robot teaching methods. Current mainstream approaches, such as manual depalletizing and overhead crane-assisted handling, suffer from low automation levels. Manual depalletizing, while meeting timing requirements, involves high labor intensity and costs, along with short-term risks of falling materials and long-term occupational hazards. To address these issues, an automated depalletizing method is proposed for tire assemblies using industrial robots and a structured light 3D camera for spatial pose recognition. The method considers the spatial stacking of tire assemblies and their pallets, as well as the specific requirements of the depalletizing process. It includes a point cloud measurement and data preprocessing procedure to ensure data integrity and computational accuracy. Since tire surfaces are not simple parametric models, the method refines point cloud boundaries by analyzing their geometric features, calculating covariance matrices, and applying normal vector filtering. A spatial pose calculation process is also designed to meet the automation needs of the depalletizing operation. We built an experimental platform using industrial robots and area-structured light 3D camera to implement the proposed measurement, calculation and depalletizing methods. Repeated tests confirmed the effectiveness of the spatial pose recognition approach in automating the tire assembly depalletizing process. The results demonstrate a 100% recognition accuracy, a repeatability precision of less than ±5 mm, an average algorithm processing time under 1.2 s for multiple tires in single layer, and a total cycle time (including measurement, computation, and robot depalletizing) of less than 13 s per workpiece. The experimental results indicate that the proposed method achieves significant improvements in efficiency, labor cost reduction, and safety risk mitigation compared to traditional approaches.

Key words: industrial robots, area-structured light, automated depalletizing

中图分类号:

TP273

王辉强, 李祥飞, 赵欢, 李楠, 袁惯喆, 丁汉. 融合协方差分析与随机采样拟合的轮胎空间位姿估计及机器人拆垛方法[J]. 机械工程学报, 2026, 62(15): 324-333.

WANG Huiqiang, LI Xiangfei, ZHAO Huan, LI Nan, YUAN Guanzhe, DING Han. Tire Pose Estimation and Robotic Depalletizing Method Integrating Covariance Analysis and Random Sample Consensus[J]. Journal of Mechanical Engineering, 2026, 62(15): 324-333.

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融合协方差分析与随机采样拟合的轮胎空间位姿估计及机器人拆垛方法

Tire Pose Estimation and Robotic Depalletizing Method Integrating Covariance Analysis and Random Sample Consensus

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