Accurate localization in industrial environments is challenging due to factors such as dust and reflections that degrade perception. To overcome these limitations, we propose an environment-independent localization method that relies solely on ultra-wideband (UWB) positioning. Our system employs LiDAR-SLAM in an offline stage to create a global map frame and calibrate the transformation between this frame and the UWB anchors. During operation, the robot estimates its position using a Kalman filter applied to UWB measurements transformed into the map frame. This paper presents a preliminary feasibility study conducted in an office-like environment to verify the core calibration and localization pipeline. The results show that the proposed method effectively aligns UWB positions with a pre-built SLAM map, achieving a 94% reduction in root-mean-square error (RMSE) compared to raw UWB measurements when validated against LiDAR-SLAM ground truth. This initial verification establishes the technical viability of the framework and lays the groundwork for future validation in harsh, large-scale industrial settings.
Automation of electronic connectors has been in demand, based on automation of assembly of electronic products. In this study, we propose a new classification of electronic connector, for grasping and assembling. We analyze characteristics of electrical connectors often used at actual industrial sites, from the perspective of the robot, not a person. As a result, it is appropriate to classify the grasp, according to the shape of the electric connector. For the assembly, we suggested that classification should be based on directions are different, because of interference of the electric wire and peripheral parts. We hope that this research will become a new basis, for electrical connector assembly.
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