Investigation of stochastic deep learning path planning methods for mobile robots
Bibliographic record
Abstract
Path planning is essential for mobile robot navigation, especially in complex environments. Traditional methods like RRT* (Rapidly Exploring Random Tree) explore known spaces effectively but lack time efficiency. Recent neural planners generate paths quickly on unseen maps, though often unreliably. This work proposes two stochastic neural planners: Noise, Displacement, Map-Generative Adversarial Network (NDM-GAN) and Stochastic-Long Short-Term Memory (S-LSTM) which integrate structured randomness to enhance generalization. NDM-GAN uses convolutions over random noise, start/goal points, and map data and S-LSTM leverages dropout in latent map-encoded LSTMs. Tested on unseen maps, they achieve up to 93.40% success and generate paths up to 13,793.18% faster than RRT*, with shorter lengths and greater obstacle clearance. Compared to similar planners, they show a 28.3% gain in viable path rates. While not probabilistically complete, these models demonstrate the power of stochasticity in neural planning, offering a strong basis for further work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".