Developing and deploying a tethered robot to map extremely steep terrain
Bibliographic record
Abstract
Abstract Mobile robots outfitted with a supportive tether are ideal for gaining access to extreme environments for mapping when human or remote observation is not possible. This paper is a field report covering both the development and field testing of our Tethered Robotic eXplorer (TReX) to map a steep, tree‐covered rock outcrop in a gravel mine. TReX is a mobile robot designed for the purpose of mapping extremely steep and cluttered environments for geologic and infrastructure inspection. In comparison to other systems, our design improves tethered mobility by enabling rotational freedom on steep slopes using a center‐pivoting tether management payload. To map the terrain, we leverage the rotation of an actuated tether spool with an attached two‐dimensional (2D) lidar, which rotates to both manage tether and produce 3D scans. Given that mapping requires vehicle motion, we also evaluate two existing, real‐time approaches to estimate the trajectory of the robot and rectify motion distortion from individual scans before alignment into the map: (a) a continuous‐time, lidar‐only approach that handles asynchronous measurements using a physically motivated, constant‐velocity motion prior, and (b) a method that computes visual odometry from streaming stereo images to use as a motion estimate during scan collection. Once rectified, individual scans are matched to the global map by an efficient variant of the Iterative Closest Point (ICP) algorithm. Our results include a comparison of estimated maps and trajectories to ground truth (measured by a remote survey station), an example of mapping in highly cluttered terrain, and lessons learned from the design and deployment of TReX.
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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 | 0.001 |
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".