A NOVEL PATH PLANNING APPROACH FOR MULTI-ROBOT BASED TRANSPORTATION
Bibliographic record
Abstract
A path planning approach has been proposed for a team of robots which formed a rigid geometry formation to navigate in the unspecified environment.It may benefit for the transportation in the logistic application.In the proposed approach, the integral rigid formation is considered as a virtual structure with virtual rigid links between robots.The path planning is based on a two-level control strategy, where the higher level controls the formation and the lower one is in charge of holding the shape of the formation.The demonstration of the proposed concept's viability has been done on the basis of both simulation and experimentation involving real robots.The simulation is performed accordingly to a scenario involving a multi-robot team including three two-wheeled robots.In the considered scenario, the three robots form a rigid formation and navigate in the constrained structure in an unmatched environment (unknown by the group of robots at the initial time step).The real robots' based demonstrator involves two two-wheeled robots within a "rigid object's transportation scenario.The considered scenario assumes that the two involved robots transport a voluminous rigid object (materialized by a long paper box in our experimental demonstration) from an initial location to a final location.Both the simulation and the experimental evaluations show that our path planning approach can be successfully applied to the logistic transportation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".