Control of swarms of autonomous robots using Model Driven Development - A state-based approach
Bibliographic record
Abstract
Unmanned vehicular systems are becoming increasingly pervasive in military and civilian applications where the tedious repetitive and hazardous nature of the tasks make them indispensable. A natural progression is to bestow autonomy upon these vehicles. In this case, the resultant robots must be able to deal with unexpected circumstances on their own and, more importantly, in real-time. As a case study we focus on swarms of robots, we define as the capability of robots to keep close to each other in formation, without colliding with neighbors and obstacles. We start by modeling and simulating a possible swarm solution in MathWorks Matlab™ and, then, moving on to change the algorithm in such a way that a controller written as a Finite State Machine (FSM) may be derived. We then use IBM Rational Rose Real-Time™ (RoseRT) to implement such a controller in emulation following the formalism of Model-Driven Development (MDD).
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".