Automatic Task Selection from Targets Recognition for Swarm Mobile Robots with Specialized Agents
Why this work is in the frame
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Bibliographic record
Abstract
Considering specialized agents in swarm robotics, with robots dedicated to specific tasks, requires that formation control and efficient transition in the leadership of the swarm is achieved. Here, a task switching approach is formulated by evolving the definition of specialization to match with targets recognition in the environment, such as detecting special landmarks via embedded sensors. Specialization zones are defined around each detected target corresponding to a task to be dealt with by a specific robot. Entering within the zone of influence surrounding a target triggers the switching of the leader of the formation. The framework is also further refined by making the targets, and therefore the corresponding zone of influence, dynamic, which leads to the consideration of combined specialization areas. The proposed system is validated in simulation to demonstrate that the group of robots effectively coordinate themselves around targets and dynamically allocate the appropriate specialized agent.
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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) | 0.001 | 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