A Method Based on Bottleneck-Linear Assignment for Forming Complex Transport Formations
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Bibliographic record
Abstract
Cooperative transportation using multi-robots is a significant challenge in robotics. For the problem, each robot is required, in general, to reach a different task-point to form a transport formation, where all the task-points are determined according to the shape of the transported object and the number of robots. A method based on bottleneck-linear assignment is proposed to form complex transport formations. First, the optimal paths from each robot to all the task-points are calculated by a two-direction path algorithm, which is developed in this paper as the core of the task-points' assignment. Second, in order to optimize the travelling paths of the robots and the time taken to establish the formation, a bottleneck-linear assignment strategy is presented to assign the task-points for the robots. Finally, an improved artificial moment motion controller makes each robot move along a sub-optimal path to reach its task-point. Simulations indicate that the proposed method is feasible and efficient.
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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 it