Vision-guided capture of a free-flying object using a redundant serial manipulator
Bibliographic record
Abstract
One important area for application of space robotics is autonomous on-orbit servicing of failed or failing spacecraft. An important aspect of these operations is the autonomous capture of the client satellite based on information obtained from a vision system. In this work, we describe laboratory experiments that verify the feasibility of autonomous capture of a slowly spinning non-cooperative satellite by a redundant serial manipulator. The main autonomous capture problem is divided in two separate tasks: the generation of a Cartesian trajectory to achieve the capture and the control of a manipulator to realize the generated Cartesian trajectory. Strategies that utilize the redundancy of a manipulator to optimize its posture are analyzed, implemented and used in the experimental validation of the autonomous capture. An online vision-based trajectory generation algorithm that generates a task-space velocity command to safely approach the target satellite and match its motion has been developed. The redundancy resolution and the trajectory generation algorithms are implemented and tested on two seven degree-of-freedom redundant manipulator systems: one located at McGill University and the other at the Canadian Space Agency. At both facilities, a scenario emulating the capture of a free-floating satellite have been created and used to validate the efficiency of the developed capture algorithm.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".