BM
Bert Monna
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Design of a Solar Array Drive Mechanism for CubeSats
A scalable, modular and reliable SADM for CubeSats in Low-Earth orbit
CubeSats have risen in popularity since its first launch in the year 2003. The low mass, lower launch cost, lower development cost, possibility for piggyback with larger satellites and lower development time involved compared to larger satellites have opened them to be commercialised by private companies. In contrast to being used for educational, research and technology demonstration purposes in their early years, they are now used for varied applications such as communications, Earth observation, military surveillance, in-orbit manufacturing, asteroid exploration, Internet-of-things and interplanetary exploration missions. Their lucrative features make them favourable over their larger counterparts. As a result, they are predicted to launch in higher numbers in the coming years, considering their preference for missions involving constellations or distributed space systems. This trend of increasing demand for CubeSats and their application in advanced missions requires more electrical power for their operation. The increased electrical power demand can be solved using a Solar Array Drive Mechanism. The SADM allows relative rotary motion of the solar arrays with respect to the satellite structure so that the solar panels are always perpendicularly positioned to the Sun independent of the payload pointing requirements. They can produce up to 185% more power than the panels just deployed in the case of a 3U CubeSat. Only a handful of six such SADM products were found in the commercial market, and four of them had a very similar design that drove two solar arrays and could be used in limited panel mounting configurations. A need in the commercial market for a SADM system that is modular and scalable was identified. Space mechanisms such as the SADM were found to be one of the major causes of mission failure after communications and unknown causes. It was found that tribological elements were the prominent root cause of such space mechanism failure. A research gap was identified to find the root causes of tribological failure in space mechanisms and design a SADM system that minimises failure caused due to tribological elements. This thesis has succeeded in designing a SADM that is scalable to multiple sizes of CubeSats (3U to 12U), applicable to more than three panel mounting configurations that were possible with the existing SADM and minimising failure chances due to tribological elements. The current SADM has minimised the chances of failure due to common tribological elements such as roller bearings and sliprings by eliminating the cause of failure. This includes eliminating rolling elements, liquid lubricants and metals in the case of bearings and a novel power and data transfer mechanism alternative to sliprings called "Flex-wrap" has been designed in this project. The current SADM is the smallest in the market in dimensions (70x50x6.9 mm) and applies to 5 different solar array mounting configurations.
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CubeSats have risen in popularity since its first launch in the year 2003. The low mass, lower launch cost, lower development cost, possibility for piggyback with larger satellites and lower development time involved compared to larger satellites have opened them to be commercialised by private companies. In contrast to being used for educational, research and technology demonstration purposes in their early years, they are now used for varied applications such as communications, Earth observation, military surveillance, in-orbit manufacturing, asteroid exploration, Internet-of-things and interplanetary exploration missions. Their lucrative features make them favourable over their larger counterparts. As a result, they are predicted to launch in higher numbers in the coming years, considering their preference for missions involving constellations or distributed space systems. This trend of increasing demand for CubeSats and their application in advanced missions requires more electrical power for their operation. The increased electrical power demand can be solved using a Solar Array Drive Mechanism. The SADM allows relative rotary motion of the solar arrays with respect to the satellite structure so that the solar panels are always perpendicularly positioned to the Sun independent of the payload pointing requirements. They can produce up to 185% more power than the panels just deployed in the case of a 3U CubeSat. Only a handful of six such SADM products were found in the commercial market, and four of them had a very similar design that drove two solar arrays and could be used in limited panel mounting configurations. A need in the commercial market for a SADM system that is modular and scalable was identified. Space mechanisms such as the SADM were found to be one of the major causes of mission failure after communications and unknown causes. It was found that tribological elements were the prominent root cause of such space mechanism failure. A research gap was identified to find the root causes of tribological failure in space mechanisms and design a SADM system that minimises failure caused due to tribological elements. This thesis has succeeded in designing a SADM that is scalable to multiple sizes of CubeSats (3U to 12U), applicable to more than three panel mounting configurations that were possible with the existing SADM and minimising failure chances due to tribological elements. The current SADM has minimised the chances of failure due to common tribological elements such as roller bearings and sliprings by eliminating the cause of failure. This includes eliminating rolling elements, liquid lubricants and metals in the case of bearings and a novel power and data transfer mechanism alternative to sliprings called "Flex-wrap" has been designed in this project. The current SADM is the smallest in the market in dimensions (70x50x6.9 mm) and applies to 5 different solar array mounting configurations.
Gathering a Machine Learning dataset for object detection from a satellite-platform
On bandwidth-efficient gathering of a Machine Learning dataset for Object Detection with Faster-RCNN from a satellite-platform
In the past years, small Earth Observation (EO) satellites have become increasingly capable of taking high-resolution images at high sample rates. These images contain valuable information for different sectors, such as the agricultural and military sector. Furthermore they can contain important information about the climate and climate change. Sending these images to earth requires a large amount of down-link bandwidth. This results in heavy, large power modules and communication modules, resulting in larger, more expensive (in terms of launch cost as well as in terms of production cost) satellites. This phenomenon already results in satellites not sending all information they gather, with examples of being able to send 2 minutes worth of data per orbit (approx. 90 minutes) not being out of the ordinary. As more and more satellites are transmitting data towards earth the communication is also expected to become even more power-intensive (or even more limited), since the (theoretically) available bandwidth per satellite is reduced. Therefore a shift towards a different approach is necessary. Smarter ways to get the relevant information to earth have to be developed. In contrast with the "common knowledge" that is often applied in the field of object detection, using the highest possible image quality does not transfer to the best trained network when gathering a dataset of satellite images, since the main constraint is the bandwidth available for transmitting images, where "normally" the largest constraint is the amount of man-hours spent on annotation. Compression of training images with JPEG-XR quality level 2 during the gathering of training images results in a better "bandwidth-efficiency", in the dataset used for this research at least up to 30000 images. It was also found that when more bandwidth is available and thus more images can be added to the training set, the optimal amount of compression tends to decrease. This results also lies in line with the result that the "final accuracy" (the predicted accuracy of a model trained on an infinite amount of images) of the models tend to improve with better image quality. From this it can be concluded that for the optimal approach the training images should be compressed as far as possible at the start of training, to then decrease the amount of compression as the mission progresses and more cumulative bandwidth is available.
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In the past years, small Earth Observation (EO) satellites have become increasingly capable of taking high-resolution images at high sample rates. These images contain valuable information for different sectors, such as the agricultural and military sector. Furthermore they can contain important information about the climate and climate change. Sending these images to earth requires a large amount of down-link bandwidth. This results in heavy, large power modules and communication modules, resulting in larger, more expensive (in terms of launch cost as well as in terms of production cost) satellites. This phenomenon already results in satellites not sending all information they gather, with examples of being able to send 2 minutes worth of data per orbit (approx. 90 minutes) not being out of the ordinary. As more and more satellites are transmitting data towards earth the communication is also expected to become even more power-intensive (or even more limited), since the (theoretically) available bandwidth per satellite is reduced. Therefore a shift towards a different approach is necessary. Smarter ways to get the relevant information to earth have to be developed. In contrast with the "common knowledge" that is often applied in the field of object detection, using the highest possible image quality does not transfer to the best trained network when gathering a dataset of satellite images, since the main constraint is the bandwidth available for transmitting images, where "normally" the largest constraint is the amount of man-hours spent on annotation. Compression of training images with JPEG-XR quality level 2 during the gathering of training images results in a better "bandwidth-efficiency", in the dataset used for this research at least up to 30000 images. It was also found that when more bandwidth is available and thus more images can be added to the training set, the optimal amount of compression tends to decrease. This results also lies in line with the result that the "final accuracy" (the predicted accuracy of a model trained on an infinite amount of images) of the models tend to improve with better image quality. From this it can be concluded that for the optimal approach the training images should be compressed as far as possible at the start of training, to then decrease the amount of compression as the mission progresses and more cumulative bandwidth is available.