A neural network approach to real-time path planning with safety consideration
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a neural network approach is proposed for real-time path planning of robots with safety consideration. The neural network is topologically organised, which is based on a previous biologically inspired model for dynamical trajectory generation of a mobile robot in a nonstationary environment. The state space of the neural network can be the joint space of multilink robot manipulators or the Cartesian workspace. This model is capable of dealing with multiple target problems as well. The target globally attracts the robot, while the obstacles push the robot away locally to avoid collisions. By taking into account of the clearance from obstacles, the planned "comfortable" path does not suffer either the "too close" or the "too far" problems. Each neuron has only local lateral connections. The optimal path is generated in real-time through the dynamics of the neural activity landscape without explicitly optimising any cost function. Therefore, it is computationally efficient. The stability of the network is guaranteed by the existence of a Lyapunov function. 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 | 0.000 | 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