堆垛搬运机器人定位误差建模及补偿方法

doi:10.3901/JME.2025.17.090

机械工程学报 ›› 2025, Vol. 61 ›› Issue (17): 90-104.doi: 10.3901/JME.2025.17.090

• 机器人及机构学 • 上一篇

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堆垛搬运机器人定位误差建模及补偿方法

汪步云^1,2, 史玉坤¹, 蒋金城¹, 程军¹, 杨鸥³, 丁慧琴⁴

1. 安徽工程大学人工智能学院芜湖 241000;
2. 芜湖云擎机器人科技有限公司芜湖 241007;
3. 徐州徐工特种工程机械有限公司徐州 220005;
4. 机器视觉检测安徽省重点实验室芜湖 241000

收稿日期:2024-02-08 修回日期:2025-04-08 发布日期:2025-10-24
作者简介:汪步云，男，1984年出生，博士，教授，硕士研究生导师。主要研究方向为机器人信息感知。E-mail：ayun@ahpu.edu.cn;史玉坤，男，1999年出生，硕士研究生。主要研究方向为智能机器人感知定位。E-mail：15391713754@163.com;蒋金城，男，1997年出生，硕士研究生。主要研究方向为智能机器人感知定位。E-mail：3287669294@qq.com;程军(通信作者)，男，1986年出生，副教授，硕士研究生导师。主要研究方向为移动机器人定位与导航。E-mail：chengjun@ahpu.edu.cn;杨鸥，男，1987年出生，高级工程师。主要研究方向为智能工业车辆。E-mail：ohyeung@foxmail.com;丁慧琴，女，1997年出生，硕士，助理工程师。主要研究方向为机器视觉检测。E-mail：1849750091@qq.com
基金资助:
安徽省经信委揭榜挂帅(JB22031)、芜湖市科技计划(2024cj40, 2024cxy24)、安徽省高校协同创新项目(GXXT-2023-076)、安徽未来技术研究院企业合作(2023qyhz35)、机器视觉检测安徽省重点实验室开放基金(KLMVI-2024-HIT-14)和安徽省智能机器人信息融合与控制工程研究中心开放课题(IFCIR2024014)资助项目。

Positioning Error Modeling & Compensation Method for Stacking Forklift Mobile Robot

WANG Buyun^1,2, SHI Yukun¹, JIANG Jincheng¹, CHENG Jun¹, YANG Ou³, DING Huiqin⁴

1. School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000;
2. Wuhu Yunqing Robotics Techology Co., Ltd, Wuhu 241007;
3. Xuzhou XCMG Special Machinery Co., Ltd, Xuzhou 220005;
4. Anhui Province Key Laboratory of Machine Vision Inspection, Wuhu 241000

Received:2024-02-08 Revised:2025-04-08 Published:2025-10-24

摘要/Abstract

摘要： 工业物流是智能制造的重要环节，堆垛搬运机器人(Forklift mobile robot, FMR)是智能工业物流系统中一种典型搬运工具，亦是移动机器人在工业场景应用的研究热点。受环境限制、作业要求和传感器测量误差等，高精度定位是搬运机器人的关键问题之一。首先，建立了搬运机器人路面激励下的运动学模型，构建了基于动力学特性参数的定位误差补偿模型；其次，提出了一种更新协方差矩阵的扩展卡尔曼数据融合方法(Update covariance matrix-extended Kalman filter, UCM-EKF)，实现轮式里程计与激光雷达位姿信息的有效融合；在此基础上，引入补偿因子，修正先验协方差矩阵，补偿累计误差，有效提升定位精度；最后，开展了搬运机器人堆垛试验，结果表明，定位误差从±33 mm提高到±13 mm，验证了所建立定位误差模型是准确的，其补偿算法有效；该方法可用于搬运机器人运动控制与导航，推动其在智能制造领域的深度应用。

关键词: 搬运机器人, 定位误差, 多传感器信息融合, 扩展卡尔曼滤波, 误差补偿

Abstract: Industrial logistics is a significant component of intelligent manufacturing. Forklift mobile robot (FMR) is a typical transporter for intelligent industrial logistics systems and a research focus of mobile robot application in industrial situations. Due to environmental restrictions, operating requirements, and measurement errors of sensors, high-precision positioning is one of the key problems of FMR. Firstly, kinematic model is established under the excitation of the travelling road surface and the positioning error model is established base on the dynamics parameters. Secondly, an update covariance matrix-extended Kalman filter (UCM-EKF) method is proposed to effectively fusion the wheel odometer and LIDAR position information. On this basis, the compensation factor is introduced to the priori covariance matrix to correct the cumulative error and effectively improve the positioning accuracy. Finally, a stacking FMR experiment is conducted to verify the method. The positioning error is reduced from ±33mm to ±13mm. According to experiment results, the positioning error model is accurate and the proposed compensation algorithm is effective. This study will support deep applications in the field of intelligent manufacturing by providing the foundation for FMR's motion control and navigation.

Key words: forklift mobile robot, positioning error, multi-sensor information introspection, extended Kalman filter, error compensation

中图分类号:

TP242

汪步云, 史玉坤, 蒋金城, 程军, 杨鸥, 丁慧琴. 堆垛搬运机器人定位误差建模及补偿方法[J]. 机械工程学报, 2025, 61(17): 90-104.

WANG Buyun, SHI Yukun, JIANG Jincheng, CHENG Jun, YANG Ou, DING Huiqin. Positioning Error Modeling & Compensation Method for Stacking Forklift Mobile Robot[J]. Journal of Mechanical Engineering, 2025, 61(17): 90-104.

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堆垛搬运机器人定位误差建模及补偿方法

Positioning Error Modeling & Compensation Method for Stacking Forklift Mobile Robot

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