Authors

Abstract

When forklifts are used to move stored crops in a storage environment, the positioning system is severely affected by the presence of multiple stored crops and shelves and other complex factors in the environment. Aiming at the problems of low positioning and navigation accuracy and large accumulated error of forklift system, a Lidar/IMU integrated navigation and positioning method is proposed in this paper, which can improve the positioning accuracy of forklift truck in storage environment. Meanwhile, the improved EKF filtering algorithm is proposed in this paper which can optimize the navigation and positioning system. This method first extracts the environmental information obtained from Lidar scan measurements and the attitude information collected by the IMU. Then the output data from the two sensors are processed with the improved EKF filtering algorithm, which can improve the navigation and positioning accuracy when the forklift is working. The Lidar/IMU integrated navigation and positioning method proposed in this paper is validated by experiments simulating forklifts working in a warehouse environment in the laboratory. Through simulation experiments, it is verified that the improved EKF filtering algorithm in this paper can improve the positioning accuracy of forklift truck, accuracy of forklift movement trajectory, closer to the expected trajectory.

Abstract in Chinese

针对电动叉车在仓库内搬运储存农作物时，在多放置架的复杂环境中导航定位不精确的问题，本文提出了一种基于激光雷达和惯性测量单元信息融合的组合导航定位系统。本文提出的方法对传统的EKF算法进行改进，通过引入影响先验协方差矩阵的因子，改变滤波方程中处理新旧数据的权重，达到抑制系统发散的预期目标，并完成对电动叉车在仓储室内位姿的精确估计。通过对机器人在室内环境中定位导航的仿真表明，该改进算法与传统算法相比，位置估计精度提高约30%，能有效提高电动叉车进行搬运储存的效率，并且能保证机器人位姿估计的鲁棒性