Real-time motion planning of car-like robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A neural network approach is proposed for real-time collision-free motion planning of holonomic and nonholonomic car-like robots in a nonstationary environment. This model is capable of planning real-time robot motion with sudden environmental changes, motion of a car with multiple targets, and motion of multiple robots. The proposed neural network model is biologically inspired, where the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. There are only local connections among neurons. The real-time optimal robot motion is planned through the dynamic neural activity landscape of the neural network without explicitly searching over the free workspace nor the collision paths, without explicitly optimizing any cost functions, without any prior knowledge of the dynamic environment, without any learning process, and without any local collision checking procedures. Therefore it is computationally efficient. The stability of the neural network is guaranteed by Lyapunov stability analysis. The effectiveness and efficiency of the proposed approach 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 | 0.000 | 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