A fuzzy set-based methodology for autonomous navigation
Bibliographic record
Abstract
This paper presents a fuzzy set-based methodology to achieve simultaneous path adherence and local collision avoidance in autonomous navigation. It targets scenarios where a global path to the vehicle's destination is already planned but with imperfect knowledge of the obstacles in the environment and their dynamics. Assuming that the vehicle can localize itself and the obstacles around it, the proposed methodology incorporates the global path and the acquired real-time knowledge of the local obstacles by the vehicle to create a comprehensive fuzzy representation of the environment. This fuzzy representation is then utilized to assess the desirability of the vehicle states within an optimization framework that balances global path adherence and obstacle avoidance objectives. The proposed gradient-based solution to this optimization problem navigates the vehicle such that it maintains its global course toward the designated destination while avoiding collision with local obstacles. Extensive simulations on a mobile robot validate the method's efficacy in guiding vehicles along desired paths, maneuvering around obstacles with minimal deviation from the route, and negotiating local minima without oscillations. Additionally, simulation results highlight the proposed fuzzy set-based methodology's low computational complexity, real-time operation capability, and adaptability in handling complex geometries of paths and obstacles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".