A neural network approach to real-time collision-free navigation of 3-DOF robots in 2D
Why this work is in the frame
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Bibliographic record
Abstract
A neural network approach to real-time collision-free navigation of holonomic 3-degree-of-freedom (DOF) robots in a nonstationary environment is proposed. This approach is based on a biologically inspired model for dynamic trajectory generation of a point robot or a multi-joint robot manipulator. The state space of the neural network is three-dimensional (3D), where two represent the spatial position in the 2D Cartesian workspace and one represents the orientation of the robot. This model is capable of generating a real-time optimal navigation path for 3-DOF robots through the dynamic neural activity landscape without explicitly optimizing any cost functions, without any learning process, and without any local collision checking procedures. Therefore it is computationally efficient. In addition, this model can deal with real-time navigation with sudden environmental changes, navigation of a robot with multiple targets, and navigation of multiple robots. The stability of the neural network is guaranteed by Lyapunov stability analysis. The effectiveness and efficiency are demonstrated through simulation studies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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