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A Cooperative Autonomous Scheduling Approach for Multiple Earth Observation Satellites With Intensive Missions

Autonomous mission scheduling of multiple earth observation satellites (multi-EOSs) is considered as a complicated combinatorial optimization problem, which requires simultaneous consideration of imaging needs, resource constraints (electricity and memory) and possible emergencies. However, EOS resources are extremely scarce relative to intensive mission observation demands and most of the existing algorithms seldom consider emergencies. To address these challenges, this paper proposes a complete multi-EOSs scheduling scheme composed of two coupling stages, including mission pre-planning and mission replanning. We aim to obtain the optimal scheduling scheme for each EOS at the same time by maximizing the observation profits and balancing the resource consumption of each EOS. In this study, the roles of solar energy and ground stations in multi-EOSs mission scheduling are also considered. In the first stage, based on the cooperation and competition mechanism as well as the dynamic adjustment approach, an evolutionary ant colony optimization (EACO) method is developed to obtain the optimal solution for multi-EOSs pre-planning. In the second stage, using the results produced by EACO, we propose an interactive replanning approach to replan the missions that cannot be performed by faulty EOS in the event of unexpected accidents. Finally, several target scenarios are designed and numerical experiments are performed to show that the proposed algorithm presents better performance for large-scale multi-EOSs missions than other state-of-the-art algorithms.