Human-Robot Collaboration in Manufacturing: A Systematic Review of Current State, Evolutionary Trajectory, and Integration Gaps
DOI:
https://doi.org/10.23055/ijietap.2026.33.5.11793Abstract
Driven by the imperative to enhance manufacturing flexibility and resilience, this study undertakes a comprehensive assessment of human-robot collaboration (HRC) technology. Its aims are threefold: to evaluate the current technological landscape, delineate its evolutionary trajectory, and pinpoint critical gaps in existing HRC implementations. The ultimate goal is to synthesize these findings into an integrated technology-effect-application reference map that researchers and practitioners can use to identify priority areas for HRC development, rather than a prescriptive integration framework. Our methodology integrates citation and cluster analysis within a systematic literature review of 121 peer-reviewed articles. Main path analysis was selected over a purely qualitative synthesis because it traces citation-based evolution using a reproducible, quantitative weighting criterion, while K-modes clustering was selected over alternatives such as K-means because it natively handles the categorical (rather than continuous) nature of the technology-effect-application coding. Citation analysis revealed HRC as a multifaceted, multi-field technology still awaiting the formation of an interconnected knowledge system. Subsequently, K-modes-based cluster analysis identified three distinct subgroups across technology, effect, and application dimensions. The analysis yielded a structured taxonomy encompassing 14 unique HRC technologies, 16 associated technological effects, and five industrial applications. Citation network analysis further exposed 11 discrete research clusters, signaling limited cross-disciplinary integration and the nascent state of HRC research interconnectivity. A two-dimensional matrix analysis pinpointed five predominant technologies propelling HRC advancement: data analytics systems, optimization and machine learning algorithms, machine vision systems, sensor networks, and human-machine interfaces. Critically, this analysis simultaneously uncovered significant technological deficiencies, notably in safety protocols and cognitive interaction frameworks, indicating areas ripe for targeted research. This research significantly contributes to the HRC domain by elucidating the contemporary state of HRC technology, charting its developmental path, and comprehensively highlighting persistent research deficiencies.
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