A Mission Architecture for On-Orbit Servicing Industrialization
Bibliographic record
Abstract
Similar to any service or product, industrialization of On-Orbit Servicing (OOS) demands performance enhancement through introducing relevant autonomy elements in planning and executing single and multiple servicing missions. This paper proposes an overall mission architecture for performing multiple on-orbit servicing missions by a fleet of servicers in the form of free-flying single-arm space manipulators. The architecture targets to improve the two key industrialization criteria of resource and service. In the far-range rendezvous with target satellites, the servicers burn most of their fuel. Furthermore, the time that servicers spend in transfer orbits determines the approximate duration of a servicing mission. Hence, as part of resource management, the presented architecture first identifies the main contributors to the fuel consumption and mission duration in far-range rendezvous phase of OOS missions being: (i) the location of the parking orbit, (ii) the type of transfer trajectories, and (iii) the dispatch scheduling. As the result, separate optimization loops are considered for minimizing the mission costs, across the OOS industry. Servicers are suggested to form an equally phased constellation in a parking orbit close to Sun-synchronous orbits in the Low Earth Orbital (LEO) region, where 57.5% of operational LEO satellites reside. A satellite in the parking orbit constellation is named “Administrator”, whose sole purpose is to plan and manage servicing missions. The Administrator determines the optimal number and sequence of servicing missions that must be performed by the available servicers, and the optimal transfer trajectories servicers shall follow to reach the targets. Upon completion of their missions, each servicer returns to the parking orbit and occupies the available position that requires the lowest fuel consumption to enter. In almost 90% of servicers' lifetime, they are in an idle state in the parking orbit awaiting dispatch or in transfer orbits. To enhance quality of the provided service, the proposed architecture suggests effective use of this time to task servicers with performing machine learning that helps improve the functionality of their guidance, navigation and control systems in upcoming missions. The task involves trajectory learning for a servicer's manipulator system in free-floating regime to reach a simulated moving target while avoiding (virtual) obstacles and compensating for environmental disturbances. Both supervised and unsupervised machine learning techniques are considered, and based on a qualitative analysis, the unsupervised DDPG algorithm is deemed most applicable in the free-floating trajectory learning task.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".