Abstract
Autonomous Mobile Robots (AMR) need a positioning function to move into unknown areas. These kinds of vehicles do not use a magnetic tape to guide into warehouses. Therefore, AMR use two different alternative techniques to solve the localization problem. First one is based on absolute positioning, and second one is established on relative localization. The absolute localization uses Simultaneous Localization and Mapping algorithms, in order to obtain a global position. However, the relative localization is based on odometry techniques. With the intention of developing a navigation system for an industrial mobile robot, which is being programmed in a structured text language, a relative localization is done utilizing LiDAR data acquisition. This novel concept analyzes two LiDAR datasets from different periods to calculate the AMR movement, by implementing Point matching and Linear Regression (LR) techniques. To understand the differences between conventional Iterative Closest Point (ICP) and LR a comparison is performed.
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Mercedes–Benz Vitoria is also acknowledged in especially to Emilio, Jose Carlos Velasco, the final assembly maintenance department of Mercedes-Benz Vitoria, Javier Loredo, Javier Gómez, Jose Antonio Hernando and Tomás Hernandez to give the opportunity to makes this research in intelligent production.
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This research was financed by the plant of Mercedes-Benz Vitoria through PIF program to develop an intelligent production. Moreover, The Regional Development Agency of the Basque Country (SPRI) is gratefully acknowledged for economic support through the research project “Motor de Accionamiento para Robot Guiado Automáticamente”, KK-2019/00099, Programa ELKARTEK. The authors are grateful to the Government of the Basque Country and to the University of the Basque Country UPV/EHU through the SAIOTEK (S-PE11UN112) and EHU12/26 research programs, respectively.
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Teso-Fz-Betoño, D., Zulueta, E., Sánchez-Chica, A., Fernandez-Gamiz, U., Uriarte, I., Lopez-Guede, J.M. (2021). A Relative Positioning Development for an Autonomous Mobile Robot with a Linear Regression Technique. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_60
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