Investigation of Stochastic Deep Learning Motion Planning Methods for Autonomous Robots
Bibliographic record
Abstract
Effective path planning is an essential part of motion planning for autonomous mobile \nrobots and vehicles. Classical sampling-based path planning methods, such as RRT* are not \ntime-efficient since they must first create an occupancy map before generating a path. Recently, neural network-based planners have been developed to solve this problem, which can \nquickly generate paths on unseen maps without an occupancy map required. However, these \nplanners perform poorly on unseen maps. To increase success on unseen maps, stochastic \nelements are included in two new neural network-based planners called Noise, Displacement, \nMap - GAN (NDM-GAN) and Stochastic-LSTM (S-LSTM). NDM-GAN performs a series \nof convolutions on a combination of random noise, the start and goal points, and the map, \nwhile S-LSTM uses an encoded map and a tensor holding the current and goal points to \nmake a path. Experiments with show that these new planners are successful between 68.58% \n- 93.40% of the time. Also, on the unseen maps, NDM-GAN and S-LSTM can generate a \npath up to 44.7897x and 379.3125x faster than RRT*, respectively. It is also shown that \npaths generated by NDM-GAN and S-LSTM often possess promising characteristics, such as \nbeing shorter than the RRT*-generated paths, and having a larger clearance from obstacles. \nSince NDM-GAN and S-LSTM are not guaranteed to find a path, if planning reliability is \nmost important, then a classical method like RRT* is preferable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".