Way-point navigation for a skid-steer vehicle in unknown environments
Bibliographic record
Abstract
Unmanned ground vehicles (UGVs) allow people to remotely access and perform tasks in dangerous or inconvenient locations more effectively. They have been successfully used for practical applications such as mine detection, sample retrieval, and exploration and mapping. One of the fundamental requirements for the autonomous operation of any vehicle is the capability to traverse its environment safely. To accomplish this, UGVs rely on the data from their on-board sensors to solve the problems of localization, mapping, path planning, and controls. This paper proposes a combined mapping, path planning, and controls solution that will allow a skidsteer UGV to navigate safely through unknown environments and reach a goal location. The mapping algorithm generates 2D maps of the traversable environment, the path planner uses these maps to find kinodynamically feasible paths to the goal, and the tracking controller ensures that the vehicle stays on the generated path during traversal. All of the algorithms are computationally efficient enough to run onboard the robot in real-time, and the proposed solution has been experimentally verified on a custom built skid-steer vehicle allowing it to navigate to desired GPS waypoints through a variety of unknown environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".