基于改进EKF算法的电动叉车组合导航定位方法

Abstract

Aiming at the problem of inaccurate navigation and positioning of electric forklifts in a complex environment with multiple placement racks when carrying stored crops in a warehouse, this paper proposes a combined navigation and positioning system based on information fusion of LiDAR and inertial measurement units. The method proposed in this paper improves the traditional EKF algorithm by introducing factors affecting the prior covariance matrix and changing the weights of processing old and new data in the filtering equation to achieve the desired goal of suppressing system dispersion and to accomplish accurate estimation of the position of electric forklifts in the storage room. The simulation of robot positioning and navigation in indoor environment shows that the improved algorithm improves the position estimation accuracy by about 30% compared with the traditional algorithm, the new algorithm can effectively improve the efficiency of electric forklift for handling and storage, and it can ensure the robustness of robot position estimation.