A real-time task-oriented scheduling algorithm for distributed multi-robot systems
Why this work is in the frame
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Bibliographic record
Abstract
Distributed multi-robot systems have attracted considerable attention over the past few decades. Multiple robots performing tasks together in a cooperative manner can have a significant advantage over a single robot, especially in parts assembly and load sharing between two or more coordinated robots. Most multi-robot systems are hard real-tune systems and require real-time scheduling. Many real-time schedulers have been discussed including round-robin, earliest-deadline-first (EDF), minimum-laxity-first (MLF), least-slack-time-first (LST), etc. Unfortunately, none of these schemes provide enough support for relative task constraints and timing constraints that are commonly used in multi-robot systems. This paper gives a task-oriented scheduling method that can help guarantee the safety, reliability and time deadline of a distributed multi-robot system. Experiments show that with the proposed algorithm, both the timing constraints and relative task interdependencies can be satisfied.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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