AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
Automotive wiring harness connector insertion—critical for vehicle assembly—has long relied on manual labor due to stringent requirements for sub-millimeter precision and robust environmental adaptability, hindering automation. Method: This paper proposes a robotized insertion method integrating real-time force control with deep visuo-tactile learning. We introduce the first multimodal Transformer architecture unifying visual, tactile, and proprioceptive sensory streams; design an expert-free pipeline for autonomous data collection and policy optimization; and generate auditable, certifiable native industrial controller code. Results: Evaluated on center-console assembly tasks, our approach reduces cycle time significantly while achieving higher insertion success rates and superior environmental robustness compared to conventional teach-pendant programming and offline trajectory planning. It delivers a production-ready intelligent manipulation paradigm for flexible automotive assembly lines.