Vision-based human–robot collaboration for wire harness assembly in automotive manufacturing

Artikel i vetenskaplig tidskrift, 2027

Wire harness assembly exemplifies a non-rigid object assembly task that remains challenging to automate due to object deformability, occlusions, high variability, and workspace constraints. This study introduces a vision-based human–robot collaboration (HRC) framework developed for wire harness assembly in automotive final assembly processes. In this system, the robot performs repetitive, force-intensive tasks, while the human operator handles operations that require dexterity and adaptive decision-making. The proposed approach combines marker-based pose estimation of wire harness components with a hand-triggered control scheme. Detected hand landmarks define a region of interest, enabling intentional, context-aware robot activation. The HRC framework is validated by performing wire harness installation on the vehicle chassis. A two-phase within-subjects experimental study compares manual installation with HRC-assisted installation in both laboratory and industrially relevant environments, corresponding to Technology Readiness Level 4 to 6. The results indicate that robotic assistance significantly reduces localized physical discomfort and physical demand while maintaining a high success rate in wire harness installation. However, overall workload does not differ significantly between HRC and manual conditions. In the HRC condition, both mental demand and average assembly time increase significantly compared to manual assembly. Cycle-time analysis reveals that the robot execution phase accounts for the largest proportion of total time in the collaborative workflow. These findings suggest that vision-based HRC can provide targeted ergonomic benefits for non-rigid object assembly, while also introducing cognitive and temporal trade-offs. Therefore, real-world deployment requires further improvement in interaction fluency and throughput. The proposed method and code are available at https://github.com/HWANG7308/ClampTracking.