Path Planning of Mobile Robot Based on Dual-Layer Fuzzy Control and Improved Genetic Algorithm
Bibliographic record
Abstract
This study addresses the dual challenges of complex road environments and diverse task-safety requirements in mobile-robot path planning by proposing an innovative method that integrates a dual-layer fuzzy control system with an improved genetic algorithm. Initially, an expert system-based dual-layer fuzzy control system is developed. The first layer translates complex road conditions and obstacles into road-safety levels, while the second layer combines these with task-safety levels to generate fitness weights for the genetic algorithm. Furthermore, road-safety factors are incorporated into the genetic algorithm’s fitness function to enhance safety considerations in path planning. The algorithm implementation incorporates Bernoulli chaotic mapping, Gaussian operators, and Symmetrical Sigmoid operators to optimize the selection, crossover, and mutation processes, significantly boosting the algorithm’s global search capability and efficiency. Experimental results indicate that the proposed method reduces path distance by up to 5.9% and decreases the number of turns by up to 85.7%, demonstrating superior universality and robustness across various comparative experiments. This research contributes to resolving the issues posed by complex road environments and varying task-safety requirements in mobile-robot path planning.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".