Online motion planning and control for autonomous on-orbit assembly with machine learning-based model predictive control
Bibliographic record
Abstract
The future of space exploration missions highly depends on autonomous space systems. To move toward autonomy, the development of online motion planners becomes a priority, where the Model Predictive Control framework is superior in this domain. At the same time, machine learning techniques open up new horizons for designing autonomous systems. This paper proposes a novel method integrating machine learning techniques with model predictive control to perform on-orbit assembly autonomously using a robotic spacecraft. In this work, a set of machine learning models, trained using the datasets obtained from the high-fidelity model developed in our previous research, are proposed to predict a set of optimization parameters in the large-scale optimal control problem to accelerate computations for online planning. More specifically, the proposed machine learning architecture is trained on a dataset generated from the shape, size, position, and orientation of space structures present in the assembly environment to predict the closest point on space structures with respect to others; hence the minimum distance. This approach reduces the number of optimization parameters in the respective optimal control problems and speeds up computations drastically. The resulting machine learning models are later utilized within a model predictive control’s prediction horizon to propose fast online motion planners. Numerical simulations demonstrate that the proposed architecture maintains a small generalization error in computing and tracking distance between objects. Finally, the online motion planners are applied to an autonomous on-orbit assembly operation using a robotic spacecraft to show the efficiency and capability of the proposed approach. • Online motion planning and control for autonomous on-orbit assembly. • Nonlinear model predictive control for online motion planning and control of robotic spacecraft. • Machine Learning incorporation into model predictive control for motion planning and control. • Contributing to proximity operations and space exploration technologies.
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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".