Optimal Planning for a Multiple Space Debris Removal Mission Using High-Accuracy Low-Thrust Transfers
Bibliographic record
Abstract
Abstract In this paper, we solve the problem of minimizing the fuel and time cost of accurate rendezvous and de-orbiting of multiple pieces of space debris in a single mission. Continuous low thrust maneuvers are used to achieve each orbital transfer. The mission scenario considered requires the chaser to capture and de-orbit the debris into a disposal orbit, after which it releases the first piece of debris and performs a rendezvous with the next piece of debris, continuing until the end of the mission in a recursive fashion. Within each rendezvous phase, the orbital drift of both the chaser and the target are considered. This is done in order to ensure the orbital elements of the chaser are matched to actual location of the debris at the end of the maneuver. Each maneuver is defined as a minimum-time orbital transfer, using low-thrust propulsion, and the transfer is posed as a constrained non-linear optimal control problem, implemented in GPOPS-II. The initial guess for the transfer time constitutes the period over which the given piece of debris is propagated to find the location of the debris after transfer. The location of the debris is then used as an initial guess for the final boundary constraint of the chaser's high-accuracy transfer. This procedure is iterated until the post-propagation location of the debris matches the location of the chaser following the high-accuracy transfer, within certain error bounds. A set of five pieces of debris with small inclination differences and a set of two pieces of debris with a large inclination difference situated in lower Earth orbit have been selected for demonstrating the proposed methodology. The outcome is the best possible trade-off between time and fuel for the multiple-debris removal mission and the transfer characteristics required to achieve it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".