Investigation of Real-Time Task Scheduling on Robot Fleets with Reconfigurable Actuators
Bibliographic record
Abstract
Multi-Fleet Scheduling (MFS) is concerned with the issue of assigning tasks to a swarm of mobile robotic agents. In this paper, MFS of tasks using a novel class of mobile agents with reconfigurable modular actuators is proposed and analyzed. MFS is split into two regimes, static and dynamic, where the static regime does not allow real-time reconfiguration of agent actuators. Most pre-existing robotic agents are compatible with the static multi-fleet scheduling (S-MFS) regime, whereas the novel agents being investigated here are capable of using dynamic multi-fleet scheduling (D-MFS). Solutions to both problems are compared, and it is shown that in the worst case scenario, given some set of agents and tasks available at known start times, D-MFS finds the same optimal schedule as S-MFS, whereas D-MFS can be used to find more optimal solutions in some conditions. It is also shown that D-MFS may not always be optimal depending on the arrival of previously unknown a-periodic tasks, as D-MFS provides the optimal schedule for a specific fleet of robots accomplishing a set of tasks for some scheduling algorithm and cost function. By defining and exploring the D-MFS problem, this work paves the way for future investigations in task-prediction, efficient large-scale scheduling algorithms, and novel robot manufacturing capabilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".