Task Scheduling of Multiple Agile Satellites with Transition Time and Stereo Imaging Constraints

This paper proposes a framework for scheduling the observation and download tasks of multiple agile satellites with practical considerations such as attitude transition time, onboard data capacity, and stereoscopic image acquisition. A mixed integer linear programming (MILP) formulation for optimal scheduling that can address these practical considerations is introduced. A heuristic algorithm to obtain a near-optimal solution of the formulated MILP based on the time windows pruning procedure is proposed. A comprehensive case study demonstrating the validity of the proposed formulation and heuristic is presented

Scheduling multiple agile Earth observation satellites with multiple observations

The Earth observation satellites (EOSs) are specially designed to collect images according to user requirements. The agile EOSs (AEOS), with stronger attitude maneuverability, greatly improve the observation capability, while increasing the complexity in scheduling. We address a multiple AEOSs scheduling with multiple observations for the first time}, where the objective function aims to maximize the entire observation profit over a fixed horizon. The profit attained by multiple observations for each target is nonlinear to the number of observations. We model the multiple AEOSs scheduling as a specific interval scheduling problem with each satellite orbit respected as machine. Then A column generation based framework is developed to solve this problem, in which we deal with the pricing problems with a label-setting algorithm. Extensive simulations are conducted on the basis of a China's AEOS constellation, and the results indicate the optimality gap is less than 3% on average, which validates the performance of the scheduling solution obtained by the proposed framework. We also compare the framework in the conventional EOS scheduling