Autonomous Satellite Launch and Assembly (SATLASS)

The project examines the development of an attachable space station module, Autonomous Satellite Launch and Assembly (SATLASS), in order to assemble and deploy customizable CubeSats in orbit. The conceptual design was optimized using quantitative and qualitative methods to ensure compatibility with modern technology and overall cost-effectiveness. Consequently, it was determined that SATLASS’s structure would be an expandable module with compound aromatic-polyamide reinforced bladder and androgynous International Berthing and Docking Mechanism (IBDM) ports which will achieve full axial expansion in five stages. Furthermore, it was established that CubeSat’s electronics and payload will be assembled using a robotic arm while a 3D printer will manufacture standardized frames and a Nanoracks CubeSat Deployer (NRCSD) will operate the deployment of the satellites. Finally, the report identifies future areas of research, such as software requirements, communication, operations, and cost and acknowledges key issues with the current design that needs to be addressed to accomplish a comprehensible SATLASS design. Currently, the report is in its first draft with a revision session to be taking place within April

Satellite Autonomous Launch and Assembly (SATLASS) Cold Gas Propulsion System and Nozzle Geometry

The Satellite Autonomous Launch and Assembly (SATLASS) project is one of the few projects within the on campus extracurricular clubs named Embry Riddle Orbital Research Association (ERORA). The members of SATLASS aim to develop a reusable cold gas propelled 3U CubeSat deployer. We aim to develop and test our cold gas propulsion system using Nitrogen gas and perform an experiment using three different nozzle geometries. The SATLASS cold gas propulsion system will be tested on its feasibility of being reusable and aid in the understanding of which nozzle geometry will reach supersonic regimes and output the amount of thrust theoretically found. Ultimately, the Nitrogen gas fueled propulsion system used in our testing will aid in the final design of our CubeSat deployer

Manufacturing Nozzle for Smoke-Generator-Type Visualization System at MicaPlex Low-Speed Wind Tunnel

Visualization of flow patterns has singularly played an essential role in the advancement of the physical understanding of fluid mechanics. Flow visualization techniques have enabled a deeper understanding of flow phenomena, the development of mathematical models for complex flow problems, the verification of existing theories as well as the design of engineering systems. Smoke visualization is a widely used method for studying the flow of fluids - particularly air - without the introduction of probes that may influence the character of the flow. Accordingly, there is a significant interest in the development of an effective smoke visualization technique at the MicaPlex Low-Speed Wind Tunnel. The present study focuses on the refurbishment of the existing smoke-generator-type visualization system at the facility to reduce mineral-oil deposits caused by the generator inside the pressurized plenum. A venturi’s nozzle was designed, manufactured, and tested as a replacement for the plenum. The design incorporated incompressible flow assumptions and featured an air compressor at the entrance, two butterfly valves, and a smoke injector at the choke point. The nozzle was printed in a masked stereolithography (MSLA) printer and tested in the facility. An iteration of the design, build, and test procedure was followed to optimize the nozzle. The engineering concluded with the successful development of a surrogate for the plenum. This innovation has the potential to create more effective flow visualization studies at the MicaPlex Low-Speed Wind Tunnel

A multi-agent evolutionary robotics framework to train spiking neural networks

A novel multi-agent evolutionary robotics (ER) based framework, inspired by competitive evolutionary environments in nature, is demonstrated for training Spiking Neural Networks (SNN). The weights of a population of SNNs along with morphological parameters of bots they control in the ER environment are treated as phenotypes. Rules of the framework select certain bots and their SNNs for reproduction and others for elimination based on their efficacy in capturing food in a competitive environment. While the bots and their SNNs are given no explicit reward to survive or reproduce via any loss function, these drives emerge implicitly as they evolve to hunt food and survive within these rules. Their efficiency in capturing food as a function of generations exhibit the evolutionary signature of punctuated equilibria. Two evolutionary inheritance algorithms on the phenotypes, Mutation and Crossover with Mutation, are demonstrated. Performances of these algorithms are compared using ensembles of 100 experiments for each algorithm. We find that Crossover with Mutation promotes 40% faster learning in the SNN than mere Mutation with a statistically significant margin.Comment: 9 pages, 11 figure

SATLASS (Satellite Autonomous Launch and Assembly)

CubeSats are small satellites composed of U’s. Each U of a CubeSat is approximately 100 x 100 x 113.5 mm and should weigh no more than 2 kg. CubeSats are predominantly used in Low Earth Orbit (LEO), but they have been expanded to use in interplanetary missions. Since the design, manufacturing, and launch of CubeSats are much cheaper than larger satellites (around $80,000 dollars for a 1U CubeSat as opposed to millions of dollars for a traditional satellite), a CubeSat is an attainable goal for a student-led team. CubeSats in the early 2000’s were university or research applications, but starting in the 2010’s they were expanded into the commercial sector. SATLASS aims to deliver three CubeSats from an initial orbit of 408.773 km (The orbit of the International Space Station) to a maximum final orbit of 478.773 km. SATLASS uses cold gas thrusters to manuver within orbits. While most other deployers operate from ground to orbit, SATLASS starts and ends its mission at a parking station, where it refuels for the next mission

Test-time adaptation with slot-centric models

Current supervised visual detectors, though impressive within their training distribution, often fail to segment out-of-distribution scenes into their constituent entities. Recent test-time adaptation methods use auxiliary self-supervised losses to adapt the network parameters to each test example independently and have shown promising results towards generalization outside the training distribution for the task of image classification. In our work, we find evidence that these losses can be insufficient for instance segmentation tasks, without also considering architectural inductive biases. For image segmentation, recent slot-centric generative models break such dependence on supervision by attempting to segment scenes into entities in a self-supervised manner by reconstructing pixels. Drawing upon these two lines of work, we propose Slot-TTA, a semi-supervised instance segmentation model equipped with a slot-centric inductive bias, that is adapted per scene at test time through gradient descent on reconstruction or novel view synthesis objectives. We show that test-time adaptation in Slot-TTA greatly improves instance segmentation in out-of-distribution scenes. We evaluate Slot-TTA in several 3D and 2D scene instance segmentation benchmarks and show substantial out-of-distribution performance improvements against state-of-the-art supervised feed-forward detectors and self-supervised test-time adaptation methods.Comment: Project website at https://mihirp1998.github.io/project_pages/slottt