Spacecraft Obstacle Avoidance and Rendezvous using Gradient Vector Fields
Bibliographic record
Abstract
Autonomous obstacle avoidance and rendezvous with tumbling targets are critical for on-orbit assembly, servicing, and debris removal. To achieve these objectives, spacecraft must employ onboard guidance algorithms that generate paths for the spacecraft to follow. This thesis presents a real-time, analytical autonomous guidance control algorithm based on the Gradient Vector Field (GVF) framework. The algorithm is designed to facilitate both obstacle avoidance and rendezvous with a tumbling target. The GVF framework is developed through a two-phase approach: Phase 1 involves the chaser spacecraft navigating to a specified radius around the target while avoiding obstacles, and Phase 2 focuses on the spacecraft maneuvering toward the docking port of the target. The effectiveness of the framework is validated through numerical simulations in both two- and three-dimensional models. Additionally, experimental validation confirms the framework’s ability to handle time-varying obstacles and perform spacecraft rendezvous. To the best of the author’s knowledge, this represents the first demonstration of such capabilities using the GVF method.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".