Sensor Fusion Approaches for Adaptive Human Robot Interaction in Dynamic Manufacturing Settings
Keywords:
Sensor fusion, Adaptive human-robot interaction, Collaborative robots, Smart manufacturing, Worker experienceAbstract
This qualitative study aims to explore sensor fusion approaches in shaping adaptive human-robot interaction in dynamic manufacturing settings. The study focuses on how workers experience, interpret, and respond to collaborative robots supported by multimodal sensor systems in automotive component manufacturing. An interpretive case study design was used at an automotive component manufacturing plant in the Karawang International Industrial City industrial area, West Java, Indonesia, from November 2025 to January 2026. Data were collected through semi-structured interviews, limited participant observation, and document analysis. The participants consisted of 18 informants, including production operators, robot technicians, automation engineers, line supervisors, a safety officer, and production managers. The data were analyzed using reflexive thematic analysis. The findings reveal five main themes: perceived safety in collaborative workspaces, trust in adaptive robot responses, changes in work coordination, operator cognitive workload, and worker acceptance of sensor-based robotic systems. The study shows that sensor fusion improves adaptive interaction by enabling robots to detect movement, adjust distance, and respond to task changes. However, worker trust and acceptance depend on response consistency, clear system feedback, practical training, and organizational communication. This study contributes to human-robot interaction literature by emphasizing sensor fusion as a sociotechnical process rather than a purely technical mechanism. The findings imply that manufacturing companies should design collaborative robot systems that are technically reliable, understandable, safe, and aligned with workers’ real work practices.
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