A neural network approach to real-time trajectory generation [mobile 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 trajectory generation in an environment with varying obstacles and moving target. This biologically inspired neural network is topologically organised. The dynamics of each neuron is characterised by a shunting equation or an additive equation. Each neuron has only local connections, and the optimal trajectories are generated without any explicitly optimising cost functions and without learning. Therefore the model is computationally efficient. The stability of the network is analytically proved using a Lyapunov function candidate. As examples, the proposed neural network is applied to trajectory formation for a mobile robot in solving maze-type problems, dynamically tracking moving target, and avoiding varying obstacles. The efficiency of the proposed approach is demonstrated through simulation and comparison 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 | 0.001 |
| 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 | 0.001 |
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