Capturing an Unknown Uncooperative Target with a Swarm of Spacecraft
Bibliographic record
Abstract
On Orbit Servicing is evolving into a critical aspect of modern space activities either for satellite maintenance, orbital assembly, or active debris removal. In this context, the capture of large unknown uncooperative targets is becoming more and more important. This paper investigates the use of a swarm of small minimalist vehicles (i.e., CubeSats, Nanosats…) as a potential solution to the problem. Swarms bring robustness, flexibility, and cost-efficiency to the system. This study presents an effective behavior-based control approach that enables the decentralized capture of a tumbling rigid body by a swarm of free-flying robots in orbit. The guidance system is inspired by bio-inspired strategies for swarm control, incorporating a flocking behavior to maneuver the robots in close proximity to the surfaces of the target body. Additionally, an anti-flocking behavior is employed to optimize the distribution of agents, ensuring comprehensive coverage of the target surfaces and encapsulation of the body shape before triggering the capture. The agents are equipped with a memory capacity that allows them to synchronize their capture on the macroscopic scale using only local individual observation of landmark points on the rigid body and communication with neighboring agents. The control is entirely decentralized, and all swarm agents are interchangeable, demonstrating a leaderless self-organizing multi-agent system. This approach demonstrates promising prospects for achieving coordinated swarm-based capture of unknown uncooperative tumbling targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".