Path planning algorithm for a South Pole lunar rover mission
Bibliographic record
Abstract
On the Moon, a rover needs to navigate around physical obstacles related to the topography such as boulders or steep slopes, to reach targets of scientific interest. In addition, it needs to avoid shadowed areas (which vary during the day), which prevent the rover from having access to the Sun for energy or to the Earth for communication. The combination of changing illumination and communication, as well as the rugged terrain, make the path planning quite complex. Moreover, the distance between the Moon and Earth causes a latency for commands to reach the rover. This makes it difficult to plan the rover’s actions, especially with many areas to explore. To tackle this problem, we propose a novel mission planner tool with two components: we first use a two-step Genetic Algorithm to compute a tentative order of exploration from a list of points of interest, allowing us to associate a time stamp for each explored waypoint. We then compute feasible trajectories between the ordered waypoints while ensuring that the rover is avoiding all static obstacles, staying in contact with Earth, and being powered by solar illumination. The simulation results show that this tool works well for different lunar sites and substantially reduces the workload for manual mission planning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".