Ant-Based Automated Mission Planning for Data Relay Space Missions

Abstract

The Earth observation (EO) market is rapidly growing due to technology miniaturization, cheaper launch opportunities, and a wider spectrum of EO applications. Along with an exponential growth of the ground-based users that can access low-Earth-orbit (LEO) spacecraft data, this growing community represents an important demand for data relay missions. LEO spacecraft have short visibility windows to the ground stations (GSs), which limits their throughput. Data relay missions comprise spacecraft at higher-altitude orbits (geostationary orbits) acting as relays of data among LEO spacecraft and GSs. Those missions are then able to offer more frequent data downlink opportunities to the LEO spacecraft, thus increasing the volume of the data reaching the ground and improving the responsiveness between users’ requests and downlink operations. Ground-based mission planning systems (MPSs) are commonly managing such complex missions, representing a large operational cost. In this paper, the application of a swarm intelligence algorithm to the design of an automated MPS for data relay missions is proposed. Automated MPSs have the potential to save operational costs while leaving the high-level decisions to human operators. This paper represents the first time that an ant colony optimization (ACO) algorithm is applied to this type of scheduling problem. This family of algorithms is generally found to offer a good level of efficiency and scalability. In this work, an ACO approach is compared against an algorithm that is popular in the literature, called Squeaky Wheel Optimization, outperforming it.