Bibliographic record
Abstract
The ability for a manipulator-equipped chaser spacecraft to autonomously capture a target spacecraft is an unsolved prerequisite for space debris removal and on-orbit servicing.This thesis investigates using deep reinforcement learning (DRL) to improve the capabilities of a manipulator-equipped chaser at this task.DRL allows for behaviour to be learned, rather than designed, according to a simple reward function.DRL uses trial-and-error to learn the behaviour, which is not feasible to perform on-board a spacecraft.Training must therefore be performed in simulation with the resulting behaviour transferred to the spacecraft.Transferring the learned-insimulation behaviour to a real robot, however, is difficult due to dynamics differences between the simulator and the real world, i.e., the simulation-to-reality gap.This thesis develops, over the course of four increasingly-difficult applications, a solution to the simulation-to-reality gap by restricting DRL to exclusively learn the guidance portion of the guidance, navigation, and control system needed for autonomous spacecraft operations.The first application is spacecraft proximity operations (without capture), where a DRL-based guidance strategy issuing desired velocity signals is designed, trained, and evaluated in simulation and experiment.Next, the DRL-based guidance strategy is improved upon and applied to a quadrotor proximity operations scenario.Here, it is demonstrated in simulation and experiment that desired acceleration signals lead to better performance compared to desired velocity signals.These two proof-of-concept results show the proposed DRL-based guidance strategy is viable for bringing DRL to real aerospace vehicles.Next, the DRL-based guidance strategy is applied to a more difficult scenario: a multi-agent cooperative quadrotor runway inspection task, where fault-tolerant behaviour is successfully learned and demonstrated in both simulation and a real, outdoor, GPS-driven quadrotor facility.Finally, with the now-developed DRL-based guidance strategy, the author returns to the central motivator for this research: autonomous manipulator-based capture of a iii spinning spacecraft.The DRL-based guidance strategy learns this task in simulation and is successfully transferred to an experimental facility where similar results are obtained.Additionally, capture is successful in experiment despite large perturbations and initial conditions not seen during training.Improvements to the experimental facility were performed to enable this research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".