Performance Improvement of UWB-sensor Positioning using Multiple Filters
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This study highlights the increasing research on transportation-robot technology in the logistics industry, smart farms, and smart factorieswith the advancement of Internet of Things technology. The current state of real-time location-tracking systems in transportationrobotsystems is analyzed, existing issues are identified, and experiments are conducted to enhance system performance. While traditionallocation-tracking technologies are expensive and complex, the ultra-wideband (UWB)-sensor precision-improvement methodproposed in this study aims to achieve high-precision positioning data at a low cost by combining multiple filters. The experiments inthis study are conducted using an integrated interface device for data processing that was designed and manufactured considering theactual 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 applyingthe filters for three types of errors: mean error, maximum error, and the number of data points exceeding a 5-cm error range. The resultsof this study are expected to be applicable to various location-based fields, such as logistics and automation robots, in the future.
Abstract
This study highlights the increasing research on transportation-robot technology in the logistics industry, smart farms, and smart factorieswith the advancement of Internet of Things technology. The current state of real-time location-tracking systems in transportationrobotsystems is analyzed, existing issues are identified, and experiments are conducted to enhance system performance. While traditionallocation-tracking technologies are expensive and complex, the ultra-wideband (UWB)-sensor precision-improvement methodproposed in this study aims to achieve high-precision positioning data at a low cost by combining multiple filters. The experiments inthis study are conducted using an integrated interface device for data processing that was designed and manufactured considering theactual 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 applyingthe filters for three types of errors: mean error, maximum error, and the number of data points exceeding a 5-cm error range. The resultsof this study are expected to be applicable to various location-based fields, such as logistics and automation robots, in the future.