An improved centroid extraction algorithm for autonomous star sensor is proposed in this study, which focuses on the improvements of the location accuracy of stars and the speed of the centroid extraction. First, the coarse positioning of stars is carried out to achieve the dispersive regions of the stars quickly. Then the stars pixels are chosen by the automatic seeded region growing algorithm. Subsequently, in order to restrain the interference of noise, the grey values of the stars pixels are modified according to the characteristics of the star energy distribution. Finally, the fine positioning of the star can be achieved using the proposed centroid calculation formula. Experimental results show that the proposed algorithm has high‐positioning accuracy and good noise resistant ability compared with the other two centroid extraction algorithms. Moreover, the computational complexity of the proposed algorithm is lower than that of the other two algorithms.

The microscope has characteristics of a planar vision with small view field and small view depth. For micro operation systems with multiple manipulators, the handling of irregular objects may lead to a nonorthogonal microscopic system, which needs to focus on clear viewing interested features, and it may also hardly locate the exact position and posture of the robot arms. In view of these, this paper proposes an active calibration method to compute image Jacobian matrix, which maps from the relative motion of the manipulators to the image coordination changes in the microscopes. We also investigate the applications in micro operator positioning, tracking for distributed systems, and movement optimization in micro-assembly. Experiments are carried out on a micro-assembly platform equipped with three microscopes and six robot arms, and the results validate the effectiveness of the proposed method.