Satellite multi -object repetitive observation mission planning based on hierarchical clustering

In order to solve the problem of large scale of feasible task execution sequences for agile satellite multi-target repetitive observation mission planning, we proposed a hierarchical clustering task planning method with time decoupling of task execution sequence. In this method, the observable windows and task execution windows in the planning process arc taken as the clustering objects, and the task set is decomposed into a series of time-decoupled small-scale sets according to the task attributes in the set by single clustering of the observable windows and multiple clustering of the task execution windows. Then,a search algorithm based on greedy optimization was used to plan the small-scale task sets generated by clustering between two task execution windows. Finally,the execution sequence of all tasks was obtained by combining the task planning results of each set. The simulation results indicate that the hierarchical clustering method can effectively reduce the global optimization complexity, eliminate the conflicts between different types of observation tasks, improve the quality of mission planning, and can perform repeated observation of multiple observation opportunities without reducing the coverage rate of the target points. The algorithm has good stability and can get planned results in a few seconds, which is suitable for satellite onboard autonomous mission planning.