Sequence Optimization in Multi-Camera Robotic Visual Inspection

Low-volume, high-mix manufacturing presents unique challenges for visual inspection, where frequent product changes make automation difficult. Manual inspection remains common but is slow and error-prone, while fully automated systems are often too costly for small and medium-sized enterprises (SMEs). We present a method to optimize the sequence of image acquisitions in robotic visual inspection. The problem is formulated as a unidirectional weighted graph and solved using Travelling Salesman Problem (TSP) techniques. Unlike prior work focused on single-camera setups, we address the more complex case of two-camera inspection with larger numbers of inspection points, introducing a geometric grouping strategy that clusters inspection points by planar regions derived from object geometry. This enables efficient parallel use of cameras while maintaining low planning complexity. The proposed framework supports agile reconfiguration of inspection tasks, making it suitable for high-mix industrial environments. In simulation of a real-world scenario, our method reduces inspection cycle times by up to 2.35× while maintaining near-optimal sequencing, demonstrating its potential to make multi-camera robotic inspection more practical for agile manufacturing.