Formation control of multiple nonholonomic mobile robots via dynamic feedback linearization
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel formation control strategy for multiple nonholonomic mobile robots based on dynamic feedback linearization and supervisory control of discrete event systems. The proposed leader-follower formation strategy requires that all the robots navigate in an unstructured environment avoiding obstacles and following walls. In addition, the followers are also required to keep a predetermined geometric formation with the leader while relaxing some of the formation constraints in the face of obstacles. Considering the nonholonomic nature of the robots involved, we use nonlinear dynamic feedback linearization to develop a set of behavior based low-level controllers to achieve proper navigation of the system. And the higher-level discrete event system manages the dynamic interaction of the robots with the external environment. The use of discrete event systems reflects a modular manageable system with the potential for scalability and reusability. The proposed system is implemented through simulation and the results are shown to verify its operation.
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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 | 0.001 |
| 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