Review of Autonomous Space Robotic Manipulators for On-Orbit Servicing and Active Debris Removal
Bibliographic record
Abstract
The increasing demand for on-orbit servicing (OOS) tasks, such as satellite repair, space debris removal, refueling, and upgrades, has driven the need for advanced robotic systems capable of autonomous and precise operations in space. At the core of these tasks are unmanned spacecraft equipped with robotic manipulators designed to execute critical capture and manipulation maneuvers. This paper presents a comprehensive review of space robotic missions and methodologies for effective OOS and space debris removal. It examines control strategies applied across different phases of these missions, with a focus on their implementation in 2 operational modes: free-floating and free-flying. Detailed discussions are provided on methodologies for the pre-capture phase, covering both motion planning and vision-based estimation. For the post-capture phase, the paper explores control methods designed to stabilize captured targets. Additionally, it investigates ground verification experiments, which are crucial for validating the performance of space robots under microgravity-like conditions. These experiments yield valuable insights into the dynamic behavior of space robotic systems and play an important role in advancing space robotics research. By consolidating recent advancements and identifying key technological gaps, this review highlights future research directions aimed at improving the reliability, adaptability, and safety of robotic manipulators in addressing the challenges of OOS and space debris removal.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".