This study investigates the visual servo control of the space station macro/micro manipulator system. The proposed approach is based on the position-based eye-in-hand visual servo (PBVS) and takes advantage of the hardware sensors to overcome the macro manipulator’s base flexibility and joint backlash. First, a vibration suppression approach based on the reaction force feedback control is proposed, the deflection forces are measured by the six-axis force/torque sensor at the base of the micro-manipulator, and damping is injected into the flexible base in the closed-loop control to suppress the base vibration. Second, the small changes of joint backlash are compensated based on the macro manipulator joint angles sensor and converted to the desired motion of the payloads. Finally, PBVS with the lag correction is proposed, which is adequate for the precise positioning of large payloads with significant low-frequency oscillations. Ground micro-gravity experiment implementation is discussed, simulations and experiments are carried out based on the equivalent 3-DOF flexible base manipulator system and the macro/micro manipulator ground facilities, and results demonstrate the effectiveness of the proposed control algorithm.

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The calibration of kinematic parameters has been widely used to improve the pose (position and orientation) accuracy of the robot arm. Intelligent measuring equipment with high accuracy is usually provided for the industrial manipulator. Unfortunately, large noise exists in the vision measurement system, which is provided for space manipulators. To overcome the adverse effect of measuring noise and improve the optimality of calibrating time, a calibration method based on extended Kalman filter (EKF) for space manipulators is proposed in this paper. Firstly, the identification model based on the Denavit–Hartenberg (D-H) modeling method is established. Then, the camera which is rigidly attached to the end-effector takes pictures of a calibration board that is settled around the manipulator. The actual pose of the end-effector is calculated based on the pictures of the calibration board. Subsequently, different data between the actual pose and theoretical pose as input, whilst error parameters are estimated by EKF and compensated in the kinematic algorithm. The simulation result shows that the pose accuracy has been improved by approximately 90 percent. Compared with the calibration method of the least squares estimate (LSE), EKF is beneficial to further optimize the calibrating time with a faster computation speed and ensure the stability of the calibration.

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