# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 1 Introduction
These methods foresee two stages: (i) the _prediction_ step, where the _proprioceptive sensors_ are used to compute a first hypothesis on the vehicle motion; (ii) the _update_ step, where the _exteroceptive sensors_ are used to correct the first hypothesis on the vehicle motion and to reduce the error by preventing trajectory drifts and unbounded growths of the localization uncertainty. In this paper, we will show an _RFID Smart Forklift_ designed to operate within a Warehouse 4.0 for tissue paper storage. The products are organized in pallets, which are tagged with passive UHF-RFID tags that allow their identification. The forklift is equipped with a series of sensors that enable its self-localization thanks to an on-site processing unit. In particular, the smart forklift is equipped with a UWB tag, which receives the signal from a set of UWB anchors installed at the warehouse ceiling, an IMU, an optical flow sensor (OFS) [10; 11], and four short-range time-of-flight laser-ranging distance sensors used to monitor the height above ground of the optical sensor. The OFS consists of a small infrared camera pointing to the floor to measure the forklift ego-motion, so it is not affected by environmental issues, such as poor light conditions or moving people and obstacles. In addition, the forklift is equipped with a UHF-RFID reader connected to four UHF-RFID antennas for pallet recognition, and an ultrasonic sensor to verify the forklift loaded/unloaded status. Through a sensor fusion tracking algorithm based on the Unscented Kalman Filter (UKF) [59], the trajectory and orientation of the forklift are reconstructed by fusing together the data acquired by the UWB system and the onboard sensors. The position of the pallets is then simply associated with the one of the forklift at the time of unloading events. All loading/unloading events and the position of the forklift over time are sent via a Wi-Fi connection to a central server for the operations management. In this way, it is possible to trace the movements of all the goods inside the warehouse. The focus of the work is to show how the proposed tracking algorithm is able to ensure good localization accuracy and a correct estimation of the vehicle orientation, which is essential to accurately determine the exact position of the unloaded pallets. 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. The paper is structured as follows: Section 2 provides an algebraic explanation of the proposed localization method, by describing the UKF design and the UWB localization technique. Section 3 shows a simulated analysis to verify the localization algorithm capability. Section 4 presents a detailed description of the experimental equipment, the performed trials, and the experimental results. Finally, Section 5 states conclusions and future works.