In this paper, we present high-resolution image and video CubeSat (HiREV), the first constructed 6 U platform to reach the space technology test bed stage, developed by the Korea Aerospace Research Institute (KARI). The CubeSat system is a low-cost platform that has been widely applied to various space missions, from missions involving earth observation to deep space. Despite the emergence of the CubeSat technology worldwide, the CubeSat market in Korea is still in the beginning stages, and a standard testing platform is also in demand. For this reason, KARI is starting to develop a 6 U CubeSat platform, which includes a less than 3 U bus system and greater than 3 U payload space. HiREV has been developed with locally manufactured parts, creating a domestic commercial off-the-shelf infrastructure for CubeSat and 3 m resolution camera payload development. Core flight software has also been applied as an on-board flight software system. Presently, we have developed the main system, while HiREV is under space environmental testing.

The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents a new approach to CubeSat constellation design for multiple missions using a multi-objective genetic algorithm (MOGA). The CubeSat constellation system is proposed to perform multi-missions that should satisfy global Earth observation and regional disaster monitoring missions. A computational approach using a class of MOGA named non-dominated sorting genetic algorithm II is implemented to optimize the proposed system. Pareto optimal solutions are found that can minimize the number of satellites and the average revisit time (ART) for both regional and global coverage while maximizing the percentage coverage. As a result, the study validates the feasibility of implementing the CubeSat constellation design with an acceptable level of performance in terms of ART and percentage coverage. Moreover, the study demonstrates CubeSat’s ability to perform a multi-missions.

A mega constellation of Nano/microsatellites is the contemporary solution for global-level Earth observation demands. However, as most of the images taken by Earth-observing satellites are covered by clouds, storing and downlinking these images results in inefficient utilization of scarce onboard resources and bandwidth. In addition, the trend of making satellite task execution plans by ground operators demands the efforts of experts or simulators to predict the real-time situation of satellites and to decide which tasks should be executed next. Granting controlled autonomy to satellites to perform onboard tasks will boost mission effectiveness. We experimented with granting controlled autonomy for satellites in performing onboard image classification and task scheduling. We designed a convolutional neural network-based binary image classification model with more than 99% accuracy in classifying clear and cloudy images. The model is configured to perform inference in low-performance computers of ordinary Cubesats. Moreover, we designed an autonomous satellite task scheduling mechanism based on reinforcement learning. It performs better than a custom heuristic-based method in scheduling onboard tasks. As a result, the proposed classification and scheduling techniques with machine learning ensured efficient utilization of onboard memory, power, and bandwidth in the highly resource-constrained CubeSat platforms and mission accomplishment of Nano/microsatellite constellations.

This paper compares and analyzes four heuristic algorithms for collision avoidance maneuver optimization (CAMO) when multiple space objects threaten a satellite. Classical gradient-based optimization methods are not appropriate for this kind of problem due to their discontinuities. On the other hand, heuristic algorithms can obtain suboptimal solutions due to their robustness and flexibility. In this paper, we develop CAMO planning methods using four heuristic algorithms. Their performance is compared in terms of the Del-V achieved under constraints on the minimum distance between the user satellite and multiple threatening objects, the maximum burn duration, and the boundary conditions for the maneuver start time. To validate the proposed strategy with the heuristic algorithms, two CAMO problems are analyzed. One is a simple problem using two control parameters (the maneuver start time and Del-V along the in-track direction) when a single threatening object is approaching. The second is a more complex CAMO problem that uses four control parameters (the maneuver start time and Del-V in three directions, i.e. radial, in-track, and cross-track) when four threatening objects are approaching from different angles and at different times. As a result, we minimize Del-V for each CAMO problem while satisfying all constraints. The differential evolution heuristic algorithm is found to exhibit the best performance in terms of minimized Del-V.