Accurate motion prediction of free-tumbling satellites is crucial for the success of capture operations. This paper proposes a two-step method to estimate the motion states and parameters of such satellites, thereby enabling precise long-term motion prediction. This paper begins with a measurement of the system’s degree of observability, quantified through the Empirical Observability Gramian (EOG). Based on this measurement, a batch processing algorithm is first employed to estimate the satellite’s constant parameters offline. Subsequently, an online filtering algorithm, utilizing a minimal state set, fine-tunes these parameters and estimates the motion states in real time. This integrated approach significantly enhances both convergence properties and estimation accuracy, particularly for systems with poor observability. Utilizing the predicted long-term motion of the satellite, a composite evaluation metric is formulated to identify the optimal capture point and moment. The base pose of the space robot is then adjusted to ensure that the optimal capture point lies within the manipulator’s dexterous workspace, which is determined through a pre-constructed capability map. The effectiveness of the proposed method is demonstrated through both simulation and experimental results.

In this paper, a visual servoing approach is developed to capture the docking rings of tumbling non-cooperative satellites with a space manipulator. The primary challenge addressed is the potential for the docking ring to leave the monocular camera’s field-of-view as the manipulator approaches the target, due to the ring’s large size. To solve this issue, a two-phase visual servoing scheme combining a monocular camera and a three-line structured light vision system is proposed. In an effort to augment the success rate and safety of capture operations, several constraints are formulated, encompassing manipulator’s kinematics, monocular camera’s field-of-view, obstacle avoidance, structured light’s breakpoints and smooth capture. Subsequently, a nonlinear model predictive controller is proposed to manage these constraints in real-time and regulate the system. System models are established based on image moments and pose for each phase, selecting these features as visual feedback to simplify the formulation of servo constraints and avoid the complex circle-based pose measurement. Furthermore, to ensure unbiased predictions, the model disturbances arising from the imprecise estimation of target motion parameter are observed using an extended Kalman filter, which are then incorporated into the predictive control framework. The simulation results demonstrate the effectiveness of this scheme.