A neural network based torque controller for collision-free navigation of mobile robots
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Bibliographic record
Abstract
In this paper, a neural network based torque controller is proposed for real-time collision-free navigation of nonholonomic mobile robots. A torque resulted from the obstacles is incorporated in the control design based on the artificial potential technique, which locally pushes the robot away from the obstacles to avoid collisions. All the needed environment information can be obtained from on-board robot sensors that have limited visibility range only. A torque from a simply single-layer neural network is employed to learn the completely unknown robot dynamics. The system stability is guaranteed by a Lyapunov stability theory. The real-time fine control of mobile robots is achieved through the on-line learning of the neural network. The effectiveness of the proposed controller is demonstrated by simulation studies in both static and dynamic environments.
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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 | 0.001 | 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