Planning method for a multi-debris active removal mission considering space debris mass

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.