Control Strategies for Reactionless Capture of an Orbiting Object using a Satellite Mounted Robot
Bibliographic record
Abstract
This paper presents a method to capture the orbiting objects using a robotic system mounted on a service satellite. The main objective is to manipulate the robot such that no reaction moment gets transferred to the base satellite. This will avoid use of any attitude controller resulting in fuel savings. Note that the constraints leading to zero reaction moment are nonholonomic, and this makes path planning a complex problem. In this work, first a method based on holonomic distribution of the nonholonomic constraints is discussed. As this method exploits constraints in terms of joint velocities, it does not always ensure successful capture. Next, a method based on task-level constraints, written in terms of end-effector's velocities, has been illustrated. It is shown that the path planned using this method has several singular points. In order to overcome disadvantages of the above two methods a novel approach is proposed which uses holonomic distribution to reach closer to the target and task-level constraints to finally capture the target. Efficacy of the method is shown using a 3-link robot mounted on a service satellite.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".