Abstract
This article addresses the problem of determining the location of pallets carried by forklifts inside a warehouse, which are recognized thanks to an onboard Radio Frequency IDentification (RFID) system at the ultra-high-frequency (UHF) band. By reconstructing the forklift trajectory and orientation, the location of the pallets can be associated with the forklift position at the time of unloading events. The localization task is accomplished by means of an easy-to-deploy combination of onboard sensors, i.e., an inertial measurement unit (IMU) and an optical flow sensor (OFS), with a commercial ultra-wideband (UWB) system through an Unscented Kalman Filter (UKF) algorithm, which estimates the forklift pose over time. The proposed sensor fusion approach contributes to the localization error mitigation by preventing drifts in the trajectory reconstruction. The designed methos was at first evaluated by means of a simulation framework and then through an experimental analysis conducted in a large warehouse with a size of about 4000 m2.
Highlights
Locating robots and other vehicles has become a key topic in recent years in the Industry 4.0 framework [1]
It must be highlighted that, the method used by the Decawave UWB system to estimate the tag location is not known to users, but it is known that no more than four UWB anchors at the same time are employed
The forklift self-localization was conducted through a sensor fusion algorithm that combined data acquired from an onboard inertial measurement unit (IMU) and an onboard optical flow sensor (OFS) with a commercial ultra-wide band (UWB) system through an unscented
Summary
Locating robots and other vehicles has become a key topic in recent years in the Industry 4.0 framework [1]. The advantages of the proposed tracking methods are as follows: (i) a single UWB tag and the onboard kinematic sensors allow to correctly reconstruct the forklift orientation; (ii) the optical flow sensor, considered here as a proprioceptive sensor, is used in combination with the UWB technology to obtain high-accuracy tracking performance; (iii) the sensor fusion scheme only foresees a single exteroceptive sensor technology for providing environmental external data (i.e., the UWB system), so it is simple and easy to install; (iv) the computational burden is low and the method can be fruitfully exploited for real-time tracking.
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