Adaptive Control of Spray Angle and Distance in Robotic Painting Systems

Abstract

Ensuring consistent paint quality in automated painting systems requires precise control of spray parameters, particularly the spray angle and the distance between the spray nozzle and the surface. This paper presents an adaptive control approach for regulating these parameters in robotic painting systems. Using feedback from depth cameras and surface normal estimation algorithms, the system maintains an optimal spray angle and distance in real time, even on complex and irregular surfaces. A PID-based control algorithm dynamically adjusts the manipulator's motion to minimize deviations, ensuring uniform paint coverage and reducing material waste. Simulation results using a ROS-based environment demonstrate significant improvements in painting accuracy, repeatability, and efficiency compared to conventional fixed-path methods. The proposed system shows strong potential for applications in flexible and small-batch industrial painting processes aligned with Industry 4.0 standards.