Optimization of small satellite constellation design for continuous mutual regional coverage with multi-objective genetic algorithm

A Simulated Annealing Algorithm with Tabu List for the Multi-Satellite Downlink Schedule Problem Considering Waiting Time

In the multi-satellite and multi-ground station downlink task scheduling problem, the waiting time from the proposal of the task to the execution will affect its validity. If the satellite has multiple communicable ground stations when the downlink task is proposed, the selection problem needs to be solved first. After the selection, since the available time conflict between tasks of different satellites for the same ground station, the specific start time should be determined. To reduce the waiting time, a simulated annealing algorithm with a tabu list and start time decision (SATLD) is proposed. This method uses a two-stage scheduling strategy. In the first stage, the improved simulated annealing algorithm based on a tabu list is used to select the downlink ground station. The second stage combines downlink scheduling algorithm based on task arrival time (DSA-AT) method and downlink scheduling algorithm based on task requirement time (DSA-RT) method to determine the specific start time of each task of a single ground station. Simulation analysis prove the method has better selection efficiency of downlink task and shorter total task waiting time, and has practical value.

Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection

The miniaturization of electronics, sensors, and actuators has enabled the growing use of nanosatellites for earth observation, astrophysics, and even interplanetary missions. This rise of nanosatellites has led to the development of an inventory of modular, interchangeable commercially-off-the-shelf (COTS) components by a multitude of commercial vendors. As a result, the capability of combining subsystems in a compact platform has considerably advanced in the last decade. However, to ascertain these spacecraft’s maximum capabilities in terms of mass, volume, and power, there is an important need to optimize their design. Current spacecraft design methods need engineering experience and judgements made by of a team of experts, which can be labor intensive and might lead to a sub-optimal design. In this work we present a compelling alternative approach using machine learning to identify near-optimal solutions to extend the capabilities of a design team. The approach enables automated design of a spacecraft that requires developing a virtual warehouse of components and specifying quantitative goals to produce a candidate design. The near-optimal solutions found through this approach would be a credible starting point for the design team that will need further study to determine their implementation feasibility.