Emerging strategies in close proximity operations for space debris removal: A review
Bibliographic record
Abstract
Space debris removal remains a leading issue for space missions due to the rapidly increasing number of objects in low earth orbit that pose a substantial risk of collision with spacecraft. Space debris removal is necessary to reduce the probability of on-orbit collisions and reduce the potential for mission failure of active spacecraft. Various active and passive methods have been proposed to remove or deorbit space debris. Active methods include the use of tentacles, robotic arms , nets, tethers, harpoons, lasers, deorbiter modules, ion beam shepherds, foam-based, and sling-sat methods, while passive methods include the use of drag sails and solar sails. While active debris removal methods have great potential, they also have inherent risks related to target fragmentation, challenges with deployment and control, and complicated close-proximity operation phases. In terrestrial applications , new strategies have emerged to address risks of fragmentation during object capture or manipulation, such as soft grippers, as well as to improve path planning and control during remote close-proximity operations, such as through virtual and mixed reality systems. This paper reviews prominent active space debris removal methods, techniques employed during close proximity capture operations, as well as promising terrestrial gripping technologies, control schemes, and teleoperation strategies for space applications. In order to effectively capture, detumble, and deorbit space debris using contact-based methods, such as robotic arms or tethered grippers, it is essential to have accurate knowledge of the debris’ geometric and inertial properties. and, thus, recent efforts in inertial parameter estimation are presented and discussed. Furthermore, operational and control strategies for space debris removal are investigated starting from traditional schemes, such as teleoperation, to emergent approaches derived from terrestrial control technologies, such as virtual and mixed reality.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".