Momentum transfer by impact: A step towards grasping of non-cooperative space debris
Bibliographic record
Abstract
The impact between a free-floating or -flying space manipulator (SM) and an uncooperative target object is studied, for the purpose of transferring momentum from the object to the SM. The impact model employed to simulate contact between the robot end-effector and the object is discrete and impulsive, and it accounts for energy dissipation during impact by using the well-known Newton’s hypothesis and energetic’s definition of the coefficient of restitution (CoR). The resulting CoR is implemented to calculate the impact force, post-impact velocity of the object, and generalized rates of the SM after the collision. The motion of the SM is controlled before and after the collision via a nonlinear optimal approach, the so-called state-dependent Riccati equation (SDRE). The SDRE is a state-feedback controller design; however, a recent modification presented a design to apply the SDRE to the output feedback control. The output- and state-dependent Riccati equation (OSDRE) uses a transformation between the output variables and the states to bypass the kinematics of a system and control the outputs directly. This led to point-to-point motion control of SMs in free-floating mode using the base to push the end-effector towards the desired position. A combination of the OSDRE and the aforementioned impact modeling is introduced in this work to transfer the energy and momentum of a non-cooperative object in space to an SM, thus achieving a nearly stationary condition of the object, amenable to subsequent capture. The specific application directs attention to the collection of uncooperative space debris, which is a prominent topic in space robotics. A planar system case study is presented to discuss and demonstrate the application in various scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".