Tethered-net capture of space tumbling debris involves complex deployment and collision processes. The ejection parameters and target characteristics are critical mission parameters in the contact process. To better assess the effects of these factors, a novel time-series predictive framework is proposed. First, an accurate dynamic model of tethered-net capture is established using the finite element method (FEM). Next, a tumbling target capture device is designed to experimentally validate the established dynamic model. Finally, a predictive model is constructed to predict the entire net contact process. It is proposed using a bidirectional long short-term memory (BiLSTM) neural network and optimized by the Bayesian optimization method. The results show that the predictive model exhibits strong generalization and high predictive accuracy, achieving 96% accuracy for the entire capture process and 98% accuracy during net deployment. Additionally, the sensitivity analysis indicates that the ejection speed and target tumbling rate are the key parameters during the contact process. This work provides valuable guidance for the design and control of future active debris removal (ADR) missions.

In this study, a combined trajectory and time-planning strategy is proposed to reduce the fuel consumption of low-thrust rendezvous missions. First, a transformation method was developed to adjust the independent variable such that results obtained using the shape-based algorithm that employs an inverse polynomial can be used as an initial guess for a pseudospectral method. Second, a planning strategy that combined low-thrust transfer trajectory optimization and boundary condition optimization was designed. This planning strategy was divided into outer and inner layers. The outer plan optimized waiting and transfer times and thus determined boundary conditions. The inner plan optimized the low-thrust transfer trajectory using the pseudospectral method. The inner plan was embedded in the outer plan. Numerical simulations show that the independent variable transformation method is feasible, and the combined use of the shape-based algorithm with the inverse polynomial and pseudospectral method ensures good convergence. The cost of rendezvous missions is significantly decreased using combined trajectory and time optimization.

In solving the problem of multi-debris active removal, the existing decision framework based on the time-dependent traveling salesman problem (TD-TSP) only optimizes the debris removal sequence and transfer time. And the effect of optimization is limited. In light of the features of multi-debris active removal missions, this study introduces the decision framework’s timing for debris release and adds debris quality as a criteria to be taken into account. This achieves a balance between the number of transfers and platform load, thus achieving optimization at a deeper level. A clustering algorithm suitable for drift orbit transfer methods is designed to select debris from a large-scale debris pool that is suitable as mission targets. This decouples the selection of debris from mission planning optimization, thereby reducing computational complexity. With reference to the orbital information of the Iridium-33 debris cloud, simulation experiments were conducted with fixed debris masses as well as with varying debris masses, which is closer to reality. The results indicate that the clustering algorithm can select targets with similar orbits and facilitate transfer. Better performance metrics have been demonstrated by the solutions derived from the decision framework presented in this research than by those derived from more conventional decision frameworks. By capturing debris in batches, the cost of the mission can be further reduced.