Falling in line: Visual route following on extreme terrain for a tethered mobile robot
Bibliographic record
Abstract
This paper describes visual route following for a cliff-climbing, tethered mobile robot for the purpose of autonomously traversing extreme terrain in the presence of obstacles. When the robot's tether contacts an obstacle, an intermediate anchor is formed. In order to detach from intermediate anchors and avoid entanglement, the robot must backtrack along its outgoing trajectory. We use the Visual Teach & Repeat (VT&R) algorithm to autonomously repeat a manually taught path. However, our problem is complicated by the fact that the robot's tether must (i) remain taut regardless of inclination, (ii) allow the robot to drive freely, and (iii) provide motion assistance when wheel traction is reduced on steep slopes. To enable visual route following over varied terrain, we have developed a novel tether controller that selects a safe, steady-state tension based on the robot's inclination while also accounting for vehicle motion. Experiments are performed on our Tethered Robotic Explorer (TReX), which autonomously repeats paths while tethered in both flat-indoor and steep-outdoor environments in the presence of obstacles.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".