Vision-Guided Fuzzy Adaptive Impedance-Based Control for Polishing Robots Under Time-Varying Stiffness

Robotic polishing is crucial for achieving superior surface finishes in manufacturing. However, precise force control presents significant challenges, particularly for curved workpieces exhibiting time-varying stiffness. Traditional methods typically struggle to adapt to these dynamic conditions, often leading to inconsistent results and suboptimal surface quality. This study proposes an Adaptive Impedance Control based on Visual Guidance (AICVG) strategy for robotic polishing. This approach integrates real-time visual feedback for geometric perception and adaptive tool path generation with a fuzzy logic system that dynamically adjusts impedance parameters to account for unforeseen surface stiffness variations. Simulations and experimental validations conducted on a robotic platform demonstrate that the AICVG strategy significantly outperforms both traditional impedance control and conventional fuzzy logic-based adaptive impedance control. Specifically, it maintains force control errors within ±1.5 N under dynamic stiffness conditions and achieves a 60% reduction in workpiece surface roughness compared to the aforementioned alternative methods. This study presents a robust and precise control framework that significantly enhances the adaptability and efficacy of robotic polishing for complex geometries, thereby advancing automated solutions in high-precision manufacturing.