Bibliographic record
Abstract
In this paper, a self-organizing map (SOM)-based multi-agent architecture is proposed for multirobot systems. It is capable of controlling a group of mobile robots to complete multiple tasks simultaneously. By cooperative and competitive behaviours, the group of mobile robots can automatically arrange the total task, and dynamically adjust their motion whenever the environment is changed. As an implementation, it can control a group of mobile robots to complete multiple tasks at different locations, such that the desired number of robots will arrive at every target location from arbitrary initial locations. The proposed approach integrates the task requirement of robots and the robot motion planning, such that the robots can start to move before their destinations are finalized. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under unexpected uncertainties, such as when some robots break down, some robots and/or some tasks are added, or some tasks are changed. Unlike most conventional models that are suitable to static environments only, the proposed approach is capable of dealing with changing environments. In addition, the proposed algorithm can be applied to the path planning of multirobot systems, where a group of robots is coordinated to visit a set of depots. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".