Deep Learning for Precise Robot Position Prediction in Logistics

Abstract

This study presents an interdisciplinary investigation at the nexus of mechanical engineering and computer science, aimed at advancing the field of logistics automation. In response to the escalating demands of global cargo transportation, the integration of these disciplines assumes paramount importance. Conducted within the domain of Dortmund University of Technology’s Material Flow and Warehousing Chair, this research focuses on the precise control of robots, a task contingent on accurate positional information. Leveraging a controlled internal logistics precinct, the study delves into the transformation of raw sensor data, comprising accelerometers, gyroscopes, and magnetometers, into precise position predictions. This process entails meticulous data preprocessing, encompassing synchronization and calibration procedures, yielding crucial parameters such as absolute velocity and accelerations along both parallel and perpendicular axes. The study employs deep learning, specifically a 2D Convolutional Neural Network (2D-CNN), for predictive modeling. This architecture excels in extracting intricate spatial features from sensor data. Training is conducted under the guidance of an Asymmetric Gaussian loss function, custom-tailored to accommodate the idiosyn- crasies of real-world sensor data. The results evince the efficacy of this approach, evidenced by remarkably low mean squared errors in predicting robot positions. Beyond its immediate applications in logistics automation, this research underscores the potential of interdisciplinary collaboration in addressing complex sensor data challenges.