Robotic navigation using harmonic function-based probabilistic roadmaps
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a new hybrid motion planning technique based on harmonic functions (HF) and probabilistic roadmaps (PRM). The proposed harmonic function based probabilistic roadmap (HFPRM) method comprises three phases: in phase one, the Laplace's equation, pertinent to potential flow, in an environment cluttered with obstacles is solved. In phase two, a probabilistic roadmap with a novel sampling scheme is constructed based on information obtained about the environment topology through the HF technique developed in phase one. The roadmap is then searched for the shortest path in phase three. Simulation results presented in this paper show that the combination of the HF and the PRM works better than each individual in terms of finding a collision free path in environments where narrow passages exist. The proposed HFPRM method can be extended to sensor-based motion planning problem in environments not known a priori.
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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 | 0.000 | 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