Optimising Automotive Wiring Harness Production Through Collaborative Robotics, Artificial Intelligence, and Digital Twins: A Structured Narrative Review

Abstract

Automotive wiring harnesses are safety-critical subassemblies whose complexity grows with driver-assistance systems, electrification, and connectivity, while pre-assembly and final assembly remain predominantly manual. This structured narrative review examines how collaborative robots (cobots), AI-based perception and planning, and Digital Twins (DTs) support harness production, with deformable-linear-object (DLO) perception and manipulation treated as a cross-cutting harness-specific domain. A database search (2015–early 2026) yielded 64 studies; citation tracking and a targeted update to 30 August 2026 added 19, giving an evidence base of 83 studies. Reported outcomes were separated into algorithm, task, operator, and system levels, and maturity was graded with explicit, review-specific Technology Readiness Level (TRL) criteria. Most evidence is at the algorithm, task, or operator level. Harness-specific cobot workstations reach industrially relevant prototype validation for single operations—for example, cobot-assisted taping with an approximately 50% reduction in ergonomic risk scores and a 12% shorter cycle—whereas harness-specific perception and DLO manipulation remain mainly at laboratory level. System-level evidence on throughput, changeover, and cost is scarce, and no continuously operating cell integrating the three core domains was identified. Key gaps concern branched DLO modelling, ergonomics-aware task planning, Human Digital Twins, connector design for automation, DT-supported safety verification, digital continuity, and reconfigurable hardware.