Identifying Worker Motion Through a Manufacturing Plant: A Finite Automaton Model

• Published in: IEEE RO-MAN pp. 1970 - 1977
• Main Authors: Yang, Shaoze, Bhat, Shreyas, Ren, Yutong, Pridham, Paul, Yang, X. Jessie, Stroup, Terra, Salour, Al
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.08.2024
• Subjects: Accuracy; Context modeling; Data models; Hidden Markov models; Layout; Learning automata; Manufacturing; Predictive models; Solid modeling; Trajectory
• DOI: 10.1109/RO-MAN60168.2024.10731360

Autonomous Guided Vehicles (AGVs) are becoming increasingly common in industrial environments to transport heavy equipment around warehouses. Within the idea of Industry 5.0, these AGVs are expected to work alongside humans in the same shared workspace. To enable smooth and trustworthy interaction between workers and AGVs, the AGVs must be able to model the workers' behavior and plan their trajectories around it. In this paper, we introduce a Finite Automaton Model (FAM) to model worker motion in such a context. We conduct a human subject experiment using a Virtual Reality (VR) environment and an omnidirectional treadmill to collect data about worker trajectories to tune our model. We show that not only is our model more interpretable, but also outperforms machine learning models at classifying worker motion behavior with limited training data. Future research can use our model to modify AGV behavior to promote trustworthy human-AGV interaction.