Abstract
Aiming at the problems of low positioning accuracy and large trajectory deviation when AGVs work in dynamic storage environments, an AGV positioning and navigation method based on multi-sensor data fusion is studied. The method is based on traceless Kalman filter (UKF) and adaptive Monte Carlo localisation (AMCL) algorithms to fuse three kinds of sensor data, namely LiDAR, inertial guidance system and ultra-wideband positioning system, to finally output the accurate attitude of the AGV [1]. Remoting data communication between the AGV and the host computer through TCP protocol, and the host computer remotely controls the AGV to achieve autonomous positioning and navigation after the sensor data is fused. Experiments are carried out on a MATLAB simulation experiment platform, and the results show that the positioning and navigation system has high positioning accuracy and trajectory fitting, and has certain practicality in dynamic storage environment.
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