Genetic algorithm-based scheduling for ground support of multiple satellites and antennae considering operation modes

Abstract

Given the unpredictability of the space environment, satellite communications are manually performed by exchanging telecommands and telemetry. Ground support for orbiting satellites is given only during limited periods of ground antenna visibility, which can result in conflicts when multiple satellites are present. This problem can be regarded as a scheduling problem of allocating antenna support (task) to limited visibility (resource). To mitigate unforeseen errors and costs associated with manual scheduling and mission planning, we propose a novel method based on a genetic algorithm to solve the ground support problem of multiple satellites and antennae with visibility conflicts. Numerous scheduling parameters, including user priority, emergency, profit, contact interval, support time, remaining resource, are considered to provide maximum benefit to users and real applications. The modeling and formulae are developed in accordance with the characteristics of satellite communication. To validate the proposed algorithm, 20 satellites and 3 ground antennae in the Korean peninsula are assumed and modeled using the satellite tool kit (STK). The proposed algorithm is applied to two operation modes: (i) telemetry, tracking, and command and (ii) payload. The results of the present study show near-optimal scheduling in both operation modes and demonstrate the applicability of the proposed algorithm to actual mission control systems.

Application of Optimal Scheduling for SAR Satellite Constellation: Multi-Imaging Mission in High-Density Regional Area

“…These approaches have been tailored to suit different satellite configurations, including both agile [13] and non-agile types [14], as well as to interact with ground stations [15]. In addition to MILP-based studies, meta-heuristic methods like Genetic Algorithms (GA) [16][17][18], Ant Colony Optimization (ACO) [19], and Particle Swarm Optimization (PSO) [20] have gained traction for complex scenarios, especially those requiring rapid response, such as natural disasters [21]. With the rise of Artificial Intelligence (AI), the field has seen a paradigm shift towards utilizing machine learning algorithms.…”

“…However, there is a relative scarcity of research focused on satellite constellations, aligning with the recent trend in satellite development. Additionally, the current body of work often relies on widely used meta-heuristic algorithms [16][17][18][19][20][21] for mission planning optimization. These algorithms, while effective in certain scenarios, tend to fall into local optima and lack consistency in producing identical results in each iteration.…”

Mixed-Integer Linear Programming Model for Scheduling Missions and Communications of Multiple Satellites

“…Equations ( 9)-( 18) are constraints on generating the schedule path of a satellite. Constraint (9) ensures the continuity of a schedule path, and Constraint (10) means that the schedule path cannot stay at a node (i, p). Constraints ( 11) and (12) indicate that a node cannot be connected with multiple nodes simultaneously.…”

“…Xhafa et al [8] have suggested a GA for ground-station allocation problems to maximize the number of communications, communication time, and communication ground stations and minimize the number of communication collisions between satellites. When the visibilities of satellites conflict with the same ground station, Lee et al [9] and Lee et al [10] have introduced a GA with a greedy method that can select a satellite while considering various factors such as priority, urgency, and profit. Luo et al [11] have suggested a heuristic algorithm combining a pre-scheduling strategy that places high-profit schedules first and a rescheduling strategy that coordinates arranged and non-placed schedules.…”

A New Strategy of Satellite Autonomy with Machine Learning for Efficient Resource Utilization of a Standard Performance CubeSat

“…A genetic algorithm (GA) is another alternative to deal with complex problems such as task scheduling. There is plenty of satellite task scheduling research based on genetic algorithms [25,26]. Mixed-Integer Linear Programming (MILP) is also another approach to making satellite task schedules [27][28][29].…”