Development of an automated industrial painting system with optimized quality and energy consumption

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Paint application is vital for product durability and aesthetics, whether done manually or by precise robotic systems. Manual work is error-prone and risky, while robots offer accuracy. However, programming robot trajectories for diverse products is challenging. Therefore, developing an autonomous system capable of generating automated paint trajectories is desirable. While adequate work has been done to optimize paint trajectories for coating thickness on complex free-form surfaces, the investigation of robot energy consumption and process time in the context of painting is left unattended. Thus, this study focuses on formulation of a hybrid optimization scheme to generate time and energy efficient paint trajectories while ensuring optimal coating deposition on a surface. Moreover, considerable effort is put into the development of hardware and software for the integrated robotic system. Results for the trajectory optimization of a car door, hood, and bumper reveal efficient paint trajectories can be obtained using the proposed optimization scheme.
Trajectory optimization, 3D scanning, Automation, Genetic Algorithm, ROS