Multi-camera joint calibration algorithm for guiding machining robot positioning considering ambient light and error uncertainty.

• A multi-camera joint calibration algorithm for guiding machining robot positioning is proposed. • The positioning error is optimized by considering both the ambient light and error uncertainty. • High dynamic range imaging is used to enhance the ability to resist the ambient light interference. • Confidence threshold filtering and deviation feedback optimization are used to solve the error uncertainty problem. • The average absolute positioning error is 1.27 mm, the optimum can reach 0.43 mm, and the average relative positioning error is 0.34 mm. Multi-camera guided robotic machining is widely used in the removal of flash and burrs on the complex automotive castings and forgings. The multi-camera calibration process, however, is easily affected by the ambient light and uncertain error, thereby resulting in the undesired robot positioning accuracy. To overcome the challenging problem, this paper proposes a multi-camera joint calibration (MCJC) algorithm by considering both the ambient light and error uncertainty to optimize and compensate the positioning error of the image-guided machining robot. The high dynamic range imaging is used at first to obtain stable calibration image data for countering the effects of ambient light changes. Then the uncertainty error in camera self-calibration process is removed by the confidence threshold filtering. The hand-eye calibration matrix is corrected finally using the deviation feedback optimization for positioning accuracy enhancement of the image-guided machining robot. Taking the automotive flywheel shell as the experimental object, the results demonstrate that the average absolute positioning error is controlled within 1.3 mm, the optimum can reach 0.43 mm, and the average relative positioning error is smaller than 0.5 mm. Compared with the existing state-of-the-art algorithms, the proposed algorithm shows high positioning accuracy and strong algorithm robustness, and can meet the requirements for the subsequent image-guided robotic machining. [ABSTRACT FROM AUTHOR]