A SENSOR-BASED NAVIGATION ALGORITHM FOR A MOBILE ROBOT USING FUZZY LOGIC
Bibliographic record
Abstract
In this paper, the goal-unreachable problems found in fuzzy logic-based algorithms for mobile robot navigation systems are studied. Two algorithms based on sensory information are developed to address problems with Goal-Unreachable with Large Obstacles (GUWLO) and Goal-Unreachable with Nearby Obstacles (GUWNO). The GUWLO problem occurs when the absolute value of the target angle is large and the directions to the left (or right) are completely blocked. This is alleviated by interpolating a temporary target angle considering the surface feature of the obstacle in front of the robot. The GUWNO problem arises because of the repulsive influence from obstacles close to the goal position. It is overcome by including an eliminator e in the fuzzy navigation system, taking into account the relative distance between the robot and its goal position. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented that demonstrate the effectiveness of the resulting fuzzy navigation system and its improved performance over conventional fuzzy logic navigation algorithms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".