Collaborative Swarm Shape Reconstruction of Tumbling Space Targets via Decentralized Dynamic Factor Graph Optimization
Bibliographic record
Abstract
On-orbit servicing (OOS) has become an essential aspect of modern space missions, encompassing satellite maintenance, orbital assembly, and debris removal. This paper presents a novel decentralized navigation algorithm designed for a swarm of servicing spacecraft to collaboratively reconstruct the shape of unknown tumbling space objects. The proposed method leverages a dynamic factor graph-based Collaborative Simultaneous Localization and Mapping (C-SLAM) framework, integrating observed and identified point cloud features across the swarm. To address the challenges associated with the target’s tumbling dynamics, the approach also incorporates a dynamic SLAM formulation, utilizing a noisy parametric model to propagate the dynamic map and construct the dynamic factor graph at the front-end. Kinematic factors are introduced to account for loop closures, enabling the swarm to recognize previously observed features as they rotate with the target, thereby enhancing mapping robustness. Additionally, the target kinematic model parameters themselves—such as its center of mass, linear velocity, and angular velocity—are estimated in real time from the reconstructed maps. The latter notably is estimated using a singular value decomposition approach that determine the best fit rotation between two consecutive sets of mapped points. These estimates are fed back into the kinematic factors and loop closure processes to refine map optimization iteratively. Simulation results demonstrate that incorporating kinematic factors, addressing loop closures, and facilitating inter-robot communication significantly enhance the swarm’s ability to track the evolving map without a central leader. This decentralized approach highlights the potential of equipping spacecraft swarms with advanced, robust, and scalable perception capabilities for the collaborative inspection and characterization of unknown tumbling space targets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".