I was, in some capacity, involved in all the research that occured at the UGA Small Satellite Research Laboratory from 2015 - 2020.
The research listed below is research that was either/or an Author, Coauthor, or Significant Contributor:
[r] The IEEE Aerospace Conference, Big Sky MT Development of a High-Performance, Heterogenous, Scalable Test-Bed for Distributed Spacecraft
DSA is building a heterogenous, many-node, Processor-in-the- Loop (PiL) testbed to aid the development and verification of scalable distributed autonomy capabilities for multi-spacecraft missions, including LPNT, heliophysics, space situational aware- ness, and collision avoidance.
Caleb Adams, Brian Kempa, Walter Vaughan, Nicholas Cramer
A simplified diagram of the 100 PiL DSA testbed
Bibtex Citation
@inproceedings{AdamsTestbed2023,
year={2023},
month=mar,
publisher={IEEE},
author={Caleb Adams, Brian Kempa, Walter Vaughan, Nicholas Cramer},
title={Development of a High-Performance, Heterogenous, Scalable Test-Bed for Distributed Spacecraft},
booktitle={2023 IEEE Aerospace Conference}
}
[r] The AIAA/Utah State Small Satellite Conference - SmallSat, Logan UT Design and testing of autonomous distributed space systems
In this paper, we present Distributed Spacecraft Autonomy (DSA) project, a payload on NASA’s Starling spacecraft experiment.
Nicholas Cramer, Daniel Cellucci, Caleb Adams, Adam Sweet, Mohammad Hejase, Jeremy Frank, Richard Levinson, Sergei Gridnev, Lara Brown
Notional example of the technology demonstration where the green lines are the selected GPS channels and the bar charts are the resulting exploit and explore rewards that get fed into the planner. Background image based on results presented
Bibtex Citation
@inproceedings{Cramer2021,
title={Design and testing of autonomous distributed space systems},
author={Nicholas Cramer, Daniel Cellucci, Caleb Adams, Adam Sweet, Mohammad Hejase, Jeremy Frank, Richard Levinson, Sergei Gridnev, Lara Brown},
url={https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=5003&context=smallsat},
journal={35th Annual AIAA/USU Conference on Small Satellites},
year={2021},
publisher={AIAA}
}
[left] The MOCI (Multiview Onboard Computational Imager) rendered. [right] The internals of the MOCI satellite, which include a 6.6 meter GSD (from 400km) optical system and a modified Nvidia TX2i GPU SoC.
[left] A 3D reconstruction generated using SSRLCV with an average accuracy of ~47 meters to the ground truth. [right] A 3D reconstruction generated using VSFM with can average accuracy of ~142 meters to the ground truth.
The recommended SSRLCV pipeline for the MOCI mission. Checkpointing should be possible at any stage or intermediate stage.
Bibtex Citation
@inproceedings{AdamsCV2021,
year={2021},
month=mar,
publisher={IEEE},
author={Caleb Adams and Jackson Parker and David Cotten},
title={A Hardware Accelerated Computer Vision Library for 3D Reconstruction Onboard Small Satellites},
booktitle={2021 IEEE Aerospace Conference}
}
-- 2020 --
[T] Master's Thesis (Final-Release), Athens GA High Performance Computation with Small Satellites and Small Satellite Swarms for 3D Reconstruction
In this thesis research I discuss the design and implementation of 2 Earth observation Cube Satellites with a focus on the computational methods used and the design of their computer systems. The satellite computer systems are tested by simulating imaging of single view observations and multiview observations. Observations are simulated by imaging existing 3D models of the Earth's surface in 3D rendering software. A custom computer vision library, known as SSRLCV, is used to compute the final 3D models which are then compared to the ground truth. Restrictions, unique to the space environment, are mitigated with a specialized operating system, hardware, and software. Tests are run on the Nvidia TX2 and TX2i with timing, state, and power usage tracking. The Nvidia TX2i GPU accelerated SoC is modified for use in a Cube Satellite and is used as the platform for high performance onboard computation. The results show accurate 3D reconstruction of the surface of Earth feasible within 15 to 100 meters, depending on the camera system and altitude, while maintaining favorable power usage and computation time.
Caleb Adams, Committee: Dr. Ramviyas Parasuraman, Dr. David Cotten, Dr. Michael E. Cotterell, Dr. WenZhan Song
[left] The MOCI (Multiview Onboard Computational Imager) rendered. [right] The internals of the MOCI satellite, which include a 6.6 meter GSD (from 400km) optical system and a modified Nvidia TX2i GPU SoC.
[left] A 3D reconstruction generated using SSRLCV with an average accuracy of ~47 meters to the ground truth. [right] A 3D reconstruction generated using VSFM with can average accuracy of ~142 meters to the ground truth.
The recommended SSRLCV pipeline for the MOCI mission. Checkpointing should be possible at any stage or intermediate stage.
Bibtex Citation
@mastersthesis{CalebAdamsMSThesis,
author={Caleb Ashmore Adams},
title={High Performance Computation with Small Satellites and Small Satellite Swarms for 3D Reconstruction},
school={The University of Georgia},
url={http://piepieninja.github.io/research-papers/thesis.pdf},
year=2020,
month=may
}
-- 2019 --
[r] The AIAA/Utah State Small Satellite Conference - SmallSat, Logan UT The Spectral Ocean Color Imager (SPOC) - An Adjustable Multispectral Imager
A pre-launch overview of the SPOC satellite's science capabilities. David L Cotten, Nicholas Neel, Deepak Mishra, Marguerite Madden, Caleb Adams, Susanne Ullrich, Adrian Burd, Malcolm Adams, Kaitlyn Summey, Casper Versteeg, Jackson Parker, Fred Beyette
Bibtex Citation
@inproceedings{SPOCsatCotten2019,
title={The Spectral Ocean Color Imager (SPOC) - An Adjustable Multispectral Imager},
author={David L Cotten, Nicholas Neel, Deepak Mishra, Marguerite Madden, Caleb Adams, Susanne Ullrich, Adrian Burd, Malcolm Adams, Kaitlyn Summey, Casper Versteeg, Jackson Parker, Fred Beyette},
url={http://smallsat.uga.edu/images/documents/papers/david_smallsat_2019_paper.pdf},
journal={33nd Annual AIAA/USU Conference on Small Satellites},
year={2019},
publisher={AIAA}
}
[r] The IEEE Aerospace Conference, Big Sky MT Towards an Integrated GPU Accelerated SoC as a Flight Computer for Small Satellites
Integration of a Nvidia TX2i module onto a PC104+ compliant stack for small satellites, additional peripherals (such as the SmartFusion2 SoC) were added. Caleb Adams, Allen Spain, Jackson Parker, Mattew Hevert, James Roach, David Cotten
[left] A system overview of the Accelerated Flight Computed design.
[right] the CORGI (Core GPU Interface) Board used for the (Multiview Oboard Computational Imager)
MOCI satellite to connect the Nvidia TX2i GPU/SoC to the rest of the Cubesat Stack.
Bibtex Citation
@inproceedings{TowardsGPUAdams2019,
doi={10.1109/aero.2019.8741765},
url={https://doi.org/10.1109/aero.2019.8741765},
year={2019},
month=mar,
publisher={IEEE},
author={Caleb Adams and Allen Spain and Jackson Parker and Matthew Hevert and James Roach and David Cotten},
title={Towards an Integrated GPU Accelerated SoC as a Flight Computer for Small Satellites},
booktitle={2019 IEEE Aerospace Conference}
}
[p] Symposium on Space Innovation, Atlanta GA GPU Accelerated SoCs as Flight Computers for Small Satellites
General information, as a poster, about the SSRLs research on using GPU accelerated SoCs as flight computers. Caleb Adams, Allen Spain, Jackson Parker, David Cotten
[s] Invited Talk - UGA Physics and Astronomy Colloquium, Athens GA What are Cubesats? A look at UGA Space Exploration.
An overview of the UGA Small Satellite Research Lab spacecraft given to physics faculty and students. Caleb Adams, Katie Summey, Nicholas Heavner
@inproceedings{NearRealTimeAdams2018,
title={A Near Real Time Space Based Computer Vision System for Accurate Terrain Mapping},
author={Caleb Adams},
url={http://piepieninja.github.io/research-papers/adamssmallsat2018.pdf},
journal={32nd Annual AIAA/USU Conference on Small Satellites},
year={2018},
publisher={AIAA}
}
[$] AFRL UNP phase A winner,
$700k phase B grant awarded for UGA's Multiview Onboard Computational Imager (MOCI), formerly the Mapping and Ocean Color Imager, Satellite
An example dense point cloud reconstruction of linne Crater on the moon.
This was a pretty good reconstruction, simulated from a satellite orbiting ~150km above the lunar surface. The simulated optical payload was the same as MOCI's.
An example of a screened poisson surface reconstruction, with varrying octree depth, of mount everest.
This was simulated with the MOCI payload, at ~400km. These are some of the expected resultes of the MOCI satellite.
An exploded view of the MOCI payload, as of August 2017. One of the payload cameras achieves ~18m GSD, the over (which is custom to the SSRL) achieves ~6.4m GSD at ~400km.
[p] NASA/CASIS International Space Station Research and Development Conference, Washington D.C. Structure from Motion from a Constrained Orbiting Platform
Using ISS image data to generate cloud height models. Caleb Adams, Nicholas (Hollis) Neel
[s] Cubesat Developers Conference - Cal Poly, San Luis Obispo CA (SP)ectral (O)cean (C)olor Satellite
An initial description of the Hyperspectral capabilities of the SPOC satellite. Caleb Adams, David Cotten, Deepak Mishra, Nicholas (Hollis) Neel, Graham Grable, Khoa Ngo
The SPOC optical train, lens housing, and exploded view of SPOCeye as it existed in the summer of 2017.
[p] UGA CURO Symposium, Athens GA Accuracy of Dense Point Clouds Given Varying Image Quality
An initial exploration of SfM from orbit using the MOCI satellite system. Nirav Ilango, David Cotten, Caleb Adams, Nicholas (hollis) Neel, Marguerite Madden, Deepak Mishra