In precision manufacturing, even microscopic surface defects can compromise product quality. While laser profilometric sensors deliver high-resolution scans, their effectiveness hinges on perfect alignment—a challenge traditionally solved through rigid robotic programming.

This research introduces a breakthrough reinforcement learning (RL) solution that dynamically optimizes sensor trajectories. Key innovations include:

• AI-driven adjustments: The PPO algorithm continuously fine-tunes sensor position and tilt during boustrophedon scans.
• Real-world simulation: A virtual environment with sensor noise and surface imperfections trains the system offline using CAD models.
• Industrial validation: Successful implementation on a UR3e robotic arm proves the method’s practicality.

By replacing static paths with adaptive, self-improving scans, this approach could redefine quality control in aerospace, automotive, and microelectronics manufacturing.