Autonomous Navigation System Design For The Free-Floating Spacecraft on a Planar Testbed
Bibliographic record
Abstract
As the International Space Exploration Coordination Group (ISECG) states, there is a growing interest in the building of a new Deep Space Habitat (DSH) near the moon, providing a microgravity research laboratory, and a refueling and maintenance stop for lunar and asteroid missions. The development of such a station requires the on-orbit self-assembly technology of large space structures due to the large size of the structures, long-distance, and harsh environment for human assembly. Considering the need for a robust and accurate control system to perform the on-orbit self-assembly task, ground testbed facilities are playing a crucial role to evaluate and validate the efficacy and efficiency of the newly-developed control systems. Although many testbeds have already been developed around the world for many purposes, including proximity operations and assembly, almost all of them suffer from the lack of full autonomy of the spacecraft simulators due to the use of external navigation systems. To address that issue, this thesis presents the research done to design a high-speed vision-based navigation system for the under-development 15 × 15 feet testbed facility, equipped with a star field overhead, at the Aerospace Innovation and Research center, Ryerson University. In this thesis, the set of image processing and computer vision algorithms, required to process the captured images, and extract the desired information from the image, is discussed. Also, the three most utilized state estimators in navigation systems, the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the Particle Filter (PF), are presented. Upon the evaluation of their performances throughout different scenarios, the EKF is chosen as the best-fitted filter for this application. To select the most appropriate computational architecture, the available computer architectures are reviewed. The combination of the parallel and serial architectures for the implementation of the algorithms is selected. This thesis also proposes an FPGA design for the implementation of the image processing and computer vision algorithms and an interface between the hardware and software to satisfy the accuracy and processing speed requirements. Ultimately, the efficacy and efficiency of the design are validated throughout the simulations and experiments using the testbed and star field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".