Enhancing Path Planning of Assistive Robots in Complex Environments Using Geno-Fuzzy Algorithm
Bibliographic record
Abstract
The path-planning algorithm is the central part of most v.The algorithm should consider fixed obstacles, furniture and building style, dynamic obstacles, humans, and pets.assistive robots encounter a challenging and complex environment with various obstacles during daily work.In addition, to maximize the service per hour, the robot has to select the optimum path.These challenges motivate the work toward an efficient path-planning algorithm that can handle complex environments.The proposed algorithm employs a designed genetic algorithm to look for the best path that maximizes the service area per hour.This genetic algorithm is then combined with a dynamic obstacle detection fuzzy system.This system relies on fuzzy membership zones.The algorithm decides whether the obstacle is dynamic or static according to speed, direction, and size.The Geno-fuzzy path planning algorithm is implemented in an assistive robot and tested in an actual environment.The algorithm implementation in a simulated environment of 100 BED hospitals in Iraq reveals a high-performance result.The test on a large scale without obstacles shows the ability of the algorithm to deal with more than 300 service points successfully.The local experiment on Webots proved the algorithm's performance to overcome dynamic obstacles and achieve safe traveling.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".