Bibliographic record
Abstract
A new method for real-time navigation of mobile robots in complex and mostly unstructured environment is presented. This novel human-inspired method (HIM) uses distance-based sensory data from a laser range finder for real-time navigation of a wheeled mobile robot in unknown and cluttered settings. The approach requires no prior knowledge from the environment and is easy to be implemented for real-time navigation of mobile robots. HIM endows the robot a human-like ability for reasoning about the situations to reach a predefined goal point while avoiding static and moving or unforeseen obstacles; this makes the proposed strategy efficient and effective. Results indicate that HIM is capable of creating smooth (no oscillations) paths for safely navigating the mobile robot, and coping with fluctuating and imprecise sensory data from uncertain environment. HIM specifies the best path ahead, according to the situation of encountered obstacles, preventing the robot to get trapped in deadlock and impassable conditions. This deadlock detection and avoidance is a significant ability of HIM. Also, this algorithm is designed to analyze the environment for detecting both negative and positive obstacles in off-road terrain. The simulation and experimental results of HIM is compared with a fuzzy logic based (FLB) approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".