Positioning Error Modeling & Compensation Method for Stacking Forklift Mobile Robot

doi:10.3901/JME.2025.17.090

Abstract

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

CLC Number:

TP242

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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