Rigorous movement of convex polygons on a path using multiple robots
Bibliographic record
Abstract
This paper describes an approach for pushing a convex polygonal object with rigor using multiple robots, along a desired rectilinear path in a two-dimensional polygonal environment. The goal is to rigorously push the object along the path while preserving its orientation and alignment, as well as precisely rotating it about its center when necessary. A path planning algorithm is presented which computes a shortest-path approximation between two points in the environment. In general, the path requires both translations and rotations of the object along the way. Robots are arranged into three groups, where each group is assigned a task of either pushing the object towards its goal or adjusting it as it veers off from the desired path. Each robot is computationally simple in that it merely moves towards a target point somewhere on the boundary of the object. As the robots move towards these target points, they cooperatively push the object with no interaction between one another. The robots rely on only three parameters to push the object: the orientation of the object, the current target point and the task they are required to perform. The target points are provided by a global control & monitoring system that monitors the progress and stability of the robots as they push the object along the path, providing direction to the robots in terms of tasks such as pushing, rotating, re-alignment, re-orientation or repositioning commands. We verified our algorithm with a number of simulations that address the usefulness of the solution as well as the effects that an increase in the number of robots will have on the runtime and the data communication load.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".