Passive versus active hazard detection and avoidance systems
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.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".