Component-Wise Error Correction Method for UWB-Based Localization in Target-Following Mobile Robot

Abstract

Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization accuracy by compensating localization bias and high-frequency deviations component by component. Initial calibration method is proposed to measure the device-dependent localization bias, which enables a compensation of the bias error not only at the calibration points, but also at the any other points. An iterative complementary filter, which recursively produces optimal estimation for each timeframe as a weighted sum of previous and current estimation depending on the reliability of each estimation, is proposed to reduce the deviation of the localization error. The performance of the proposed method is validated using simulations and experiments. Both the magnitude and deviation of the localization error were significantly improved by up to 77 and 51%, respectively, compared with the previous method.

Advances in Indoor Positioning and Indoor Navigation

“…Another challenging scenario is robot localization, for example, in Bae et al [ 4 ], since they usually need high accuracy. Once again, we find that UWB can compensate bias.…”

Longitudinal control for person-following robots

“…Calculation speed, stability and low latency are critical to a basic following control unit. The complex following control unit manages the advanced functions of PFR, such as analyzing data from environment perception devices, obstacle avoidance, routing planning, gesture identification for interaction with the LP and interaction with other PFRs in a team following scenario (Bae et al , 2022 ; Austria et al , 2021 ; Minaeian et al , 2017). High computation power is desirable for complex tasks, such as image recognition and AI analysis.…”

“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2399-9802.htm interaction with other PFRs in a team following scenario (Bae et al, 2022;Austria et al, 2021;Minaeian et al, 2017). High computation power is desirable for complex tasks, such as image recognition and AI analysis.…”