FreeD∗: a mechanism for finding a short and collision free path
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
Path planning is extensively used in different fields not only in robotics but also in games, manufacturing, auto‐motive applications, and so on. Robot path planning is one of the major research issues in the area of autonomous mobile robot. The critical step in path planning problem is to find the shortest path from the start position to a defined goal position through a known, unknown, or partially known environment. Hazardous events that may devastate some parts of the intended area convert those areas to untraversable areas. These events introduce topological constraints for the robot motion because of information discrepancy about the environment before and after the damage. In this study, the authors propose a novel method, FreeD∗, to find the shortest path by exploiting the benefits of D∗, Dijkstra, and artificial potential field (APF) algorithms. The generated path using D∗ is optimised using Dijkstra by combining D∗ sub‐paths into a single diagonal path if there is no known obstacle between them. Then, APF is used in unknown obstacle avoidance. The simulation results using Webots simulator demonstrate the effectiveness of FreeD∗ in avoiding unknown obstacles with shortest path.
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.
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 | 0.001 | 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 | 0.001 | 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