Validation of Autonomous Hazard-Avoidance Mars Landing via Closed-Loop Simulations
Bibliographic record
Abstract
Future planetary exploration missions will aim at landing a spacecraft in hazardous regions of a planet, thereby requiring the ability to autonomously perform the critical landing operations, including the avoidance of surface obstacles and the identification of the best landing site. These algorithms are rather complex and cannot be tested easily at moderate cost on a real system. Therefore, closed-loop software simulator tools are required to validate these new technologies, generate new advancements and help plan for future planetary exploration missions. This paper presents a closed-loop planetary landing simulator and its use in the validation of the hazard-avoidance landing technologies. The simulator emulates the real world with a 7-degree-offreedom landing dynamics, models of sensors and actuators and it closes the control loop with the on-board software that provides the “intelligence” to the landing vehicle. The paper will provide an overview of the Lidar-based guidance, navigation and control functions and it will demonstrate their successful validation through Monte Carlo simulations using the simulator adapted to a Mars landing mission.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".