Spacecraft Obstacle Avoidance and Rendezvous using Gradient Vector Fields
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it