Multi-Debris Capture by Tethered Space Net Robot via Redeployment and Assembly
Bibliographic record
Abstract
This paper presents an advanced multiple pieces of debris capture method for the tethered space net robot. A novel capture strategy that integrates redeployment and self-assembly techniques is introduced. With this strategy, the net robot self-assembles to close the net pocket and quickly maneuvers to nearby target debris. Upon reaching its target, the robot redeploys the net for debris capture. Central to this approach is the development of a capturing model that accurately describes the state function of the deployed net and incorporates an effective dragging method to counteract debris bouncing. To enable this multiple pieces of debris capture, an attitude consensus controller and a capture controller are designed using a finite-time scheme and terminal sliding mode, respectively. Numerical simulations reveal limitations in capturing multiple pieces of debris by existing design of tethered space net robots, where debris has the propensity to bounce out of a fully deployed net during multicapture attempts. The proposed strategy effectively mitigates these challenges, minimizing debris bounce and ensuring dependable multiple pieces of debris capture. Overall, the findings offer valuable insights to enhance the efficiency of active space debris removal missions.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".