Flocking motion control for a system of nonholonomic vehicles
Why this work is in the frame
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Bibliographic record
Abstract
The applications of nonholonomic autonomous systems, such as smart cars and wheeled domestic robots, have gained much attention in the scientific and industrial communities. The non-linearities in the kinematics and dynamics of such systems pause many challenges in stabilizing their motion. In this paper, a motion control system with local and team control objectives is introduced for a flock of nonholonomic vehicles, using bounded input-output feedback linearization. The team control objectives include a navigational control system, which is implemented using smooth state feedback control laws, and a synchronization control system, which is implemented using a smooth position dependent adjacency matrix and communication graph structures. The local control objective involves a collision avoidance scheme, which is implemented using an extended Takagi-Sugeno-Kang fuzzy model. The proposed technique is successfully validated in a numerical simulation with 10 differentialdrive mobile robots.
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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) | 0.000 | 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