An improved SOM-based approach to dynamic task assignment of multi-robots
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Bibliographic record
Abstract
In this paper, an improved self organizing map (SOM)-based approach is proposed for multi-robot systems to tackle the task assignment problem which focuses on the self-organization issue with a large number of robots and a large number of task locations in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve different task locations from arbitrary initial locations and directions. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The group of mobile robots can automatically arrange the total task, and dynamically adjust their motion whenever the environment is changed, such as when some robots break down, some robots and/or some tasks are added, or the situation accruing when some tasks are changed. Different from our early study, the current direction of every robot is considered during the robot motion planning. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
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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 | 0.000 | 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 | 0.000 | 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