Model Development for Autonomous Short-Term Adaptation of Cobots' Motion Speed to Human Work Behavior in Human-Robot Collaboration Assembly Stations
Abstract
Manufacturing flexibility and human-centered designs are promising approaches to face the demand for individualized products. Human-robot assembly cells still lack flexibility and adaptability (VDI, 2017) using static control architectures (Bessler et al., 2020). Autonomous adaptation to human operators in short time horizons increases the willingness to work with cobots. Besides, monotonous static assembling in manufacturing operations does not accommodate the human way of working. Therefore, Human-Robot Collaboration (HRC) workstations require a work behavior adaptation accommodating varying work behavior regarding human mental and physical conditions (Weiss et al., 2021). The thesis presents the development of a cyber-physical HRC assembly station. Moreover, the thesis includes an experimental study investigating the influence of a cobot’s speed on human work behavior. The Cyber-Physical System (CPS) integrates the experiment's findings with event-based software architecture and a semantic knowledge representation. Thereby, the work focuses on demonstrating the feasibility of the CPS and the semantic model, allowing the self-adaptation of the system. Finally, the conclusion identifies the need for further research in human work behavior detection and fuzzy decision models. Such detection and decision models could improve self-adaptation in human-centered assembly systems.
Degree
M.Sc.
Advisors
Athinarayanan, Purdue University.
Subject Area
Design|Industrial engineering
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