Planning using a Network of Reusable Paths: A Physical Embodiment of a Rapidly Exploring Random Tree
Bibliographic record
Abstract
Growing a network of reusable paths is a novel approach to navigation that allows a mobile robot to autonomously seek distant goals in unmapped, GPS‐denied environments, which may make it particularly well‐suited to rovers used for planetary exploration. A network of reusable paths is an extension to visual‐teach‐and‐repeat systems; instead of a simple chain of poses, there is an arbitrary network. This allows the robot to return to any pose it has previously visited, and it lets a robot plan to reuse previous paths. This paradigm results in closer goal acquisition (through reduced localization error) and a more robust approach to exploration with a mobile robot. It also allows a rover to return a sample to an ascent vehicle with a single command. We show that our network‐of‐reusable‐paths approach is a physical embodiment of the popular rapidly exploring random tree (RRT) planner. Simulation results are presented along with the results from two different robotic test systems. These test systems drove over 14 km in planetary analog environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".