Abstract
This study highlights the increasing research on transportation-robot technology in the logistics industry, smart farms, and smart factories with the advancement of Internet of Things technology. The current state of real-time location-tracking systems in transportation-robot systems is analyzed, existing issues are identified, and experiments are conducted to enhance system performance. While traditional location-tracking technologies are expensive and complex, the ultra-wideband (UWB)-sensor precision-improvement method proposed in this study aims to achieve high-precision positioning data at a low cost by combining multiple filters. The experiments in this study are conducted using an integrated interface device for data processing that was designed and manufactured considering the actual environment. Real-time positioning data are obtained by applying various filters, including the Kalman, Median, and Min filters, to a real-time positioning system using UWB sensors. The acquired data are analyzed by comparing the results before and after applying the filters for three types of errors: mean error, maximum error, and the number of data points exceeding a 5-cm error range. The results of this study are expected to be applicable to various location-based fields, such as logistics and automation robots, in the future.