Passive versus active hazard detection and avoidance systems
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
Upcoming planetary exploration missions will require advanced guidance, navigation and control technologies to reach landing sites with high precision and safety. Various technologies are currently in development to meet that goal. Some technologies rely on passive sensors and benefit from the low mass and power of such solutions while others rely on active sensors and benefit from an improved robustness and accuracy. This paper presents two different hazard detection and avoidance (HDA) system design approaches. The first architecture relies only on a camera as the passive HDA sensor while the second relies, in addition, on a Lidar as the active HDA sensor. Both options use in common an innovative hazard map fusion algorithm aiming at identifying the safest landing locations. This paper presents the simulation tools and reports the closed-loop software simulation results obtained using each design option. The paper also reports the Monte Carlo simulation campaign that was used to assess the robustness of each design option. The performance of each design option is compared against each other in terms of performance criteria such as percentage of success, mean distance to nearest hazard, etc. The applicability of each design option to planetary exploration missions is also discussed.
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