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Stochastic Scheduling for Human-Robot Collaboration in Dynamic Manufacturing Environments

Fulltext:


Authors:

Anders Lager, Branko Miloradovic, Giacomo Spampinato , Thomas Nolte, Alessandro Papadopoulos

Research group:

Complex Real-Time Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

34th IEEE International Conference on Robot and Human Interactive Communication

Abstract

Collaborative human-robot teams enhance efficiency and adaptability in manufacturing, but task scheduling in mixed-agent systems remains challenging due to the uncertainty of task execution times and the need for synchronization of agent actions. Existing task allocation models often rely on deterministic assumptions, limiting their effectiveness in dynamic environments. We propose a stochastic scheduling framework that models uncertainty through probabilistic makespan estimates, using convolutions and stochastic max operators for realistic performance evaluation. Our approach employs metaheuristic optimization to generate executable schedules aligned with human preferences and system constraints. It features a novel deadlock detection and repair mechanism to manage cross-schedule dependencies and prevent execution failures. This framework offers a robust, scalable solution for real-world human-robot scheduling in uncertain, interdependent task environments.

Bibtex

@inproceedings{Lager7217,
author = {Anders Lager and Branko Miloradovic and Giacomo Spampinato and Thomas Nolte and Alessandro Papadopoulos},
title = {Stochastic Scheduling for Human-Robot Collaboration in Dynamic Manufacturing Environments},
month = {August},
year = {2025},
booktitle = {34th IEEE International Conference on Robot and Human Interactive Communication},
url = {http://www.es.mdu.se/publications/7217-}
}