GA‐based camera calibration for vision‐assisted robotic assembly system

Vision sensors are employed in robotic assembly system to sense the dynamic environment and to position the manipulator precisely based on the sensor feedback. This process is termed as visual servoing. Precise calibration of the camera and camera/robot system are required to estimate the desired velocity of the robot and accurate positioning of the mating parts. In position‐based visual servoing, roughly calibrated camera leads to errors in robot/camera pose identification that affects the positional accuracy and time to reach the target position. A camera calibration procedure based on genetic algorithm (GA) is proposed in this study to estimate the intrinsic and extrinsic parameters of the camera model for improving positional accuracy and faster convergence. The proposed algorithm is implemented with two‐stage procedure and it comprises: determination of the camera parameters for distortion‐less model and reduction of re‐projection error through GA with linearly determined camera distortion‐less parameters as an initial solution. The proposed camera calibration algorithm has been tested and compared with the dataset images in the literature for its performance in terms of measurement accuracy. The result shows that the proposed algorithm has the capability to calibrate the distorted images with minimum re‐projection error using single image