# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 5 Conclusions  
In this paper, a system consisting of an RFID-equipped forklift that is able to recognize the loaded pallets and to self-localize was addressed for a tissue paper factory warehouse. Thanks to the forklift trajectory and orientation reconstruction, the location of the pallets can be associated with the forklift position at the time of unloading events. 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 Kalman filter (UKF) estimator. The proposed method is relevant to the state-of-the-art because of the usage of the optical flow sensor as an onboard proprioceptive sensor to measure the forklift ego-motion, which allows the employment of an optical domain sensor without the disadvantage of the dependence from environmental light conditions. After a first numerical investigation of the method performance, the whole system was evaluated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Case** & **Trial Duration [s]** & **Total Elaboration Time [s]** & **Number of Samples** & **Elaboration Time for Sample [ms]** \\ \hline I & 136 & 10.81 & 13,641 & 0.79 \\ II & 132 & 10.05 & 13,265 & 0.75 \\ III & 63 & 5.23 & 6369 & 0.82 \\ IV & 64 & 5.18 & 6432 & 0.8 \\ V & 36 & 3.43 & 3606 & 0.95 \\ VI & 36 & 3.34 & 3611 & 0.92 \\ VII & 114 & 9.28 & 11,346 & 0.81 \\ VIII & 110 & 9.05 & 11,038 & 0.82 \\ IX & 225 & 17.09 & 22,563 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Elaboration Time compared to trial duration for the test cases I–IX.  
Figure 17: Histogram of the orientation error \(\epsilon_{\theta}\) for the test cases I–IX.  
in a 4560 m\({}^{2}\) warehouse where thirty Decawave commercial UWB anchors were installed at the ceiling, at known locations. Nine different test case trajectories were performed with different paths and forklift speed in order to investigate the sensor robustness to different operating conditions. The experimental analysis showed a decrease of the performance of onboard kinematic sensors and the UWB system with increasing forklift speed, which can be mitigated by the usage of the UKF. Indeed, the proposed method achieved a global experimental mean localization error of 1 m, with a maximum error of 4 m, and the orientation of the forklift was reconstructed with a mean error of 0.014 rad. Moreover, the UKF resulted in being \"robust\", with respect to different initialization conditions, even for long paths (almost 300 m). An analysis of the UKF computational burden suggests the real-time feasibility of the proposed method. The achieved localization performance together with a limited computational burden makes the proposed low-cost, robust, and easy-to-deploy system suitable, not only for real-time monitoring of the warehouse status, but also as a potential candidate to be tested for collision avoidance systems.  
Conceptualization, A.M.; software, A.M.; methodology, A.M., A.B., P.N.; validation, A.M., A.B.; resources, P.N.; writing, A.M., A.B., P.N.; review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Regione Toscana (FOR FESR 2014-2020--Line 1 Research and Development Strategic Projects) through the Project IREAD4.0 under grant CUP 7165.24052017.112000028, and in part by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence). Info: [PERSON] (e-mail: [EMAIL_ADDRESS]).  
Not applicable.  
The authors wish to thank the company Sofidel S.p.a., Porcari, Italy, in particular [PERSON] and [PERSON]. A grateful \"thank you\" also goes to ISE s.r.l. (_Ingegneria dei Sistemi Elettronici_, Vecchiano, Italy, for their precious technical support.  
The authors declare no conflict of interest.