GAA-DFQ: A Dual-Layer Learning Model for Robot Path Planning in Dynamic Environments Integrating Genetic Algorithms, DWA, Fuzzy Control and O-Learning

摘要

This paper presents a dual-optimization learning model combining genetic algorithms for global path planning with local obstacle avoidance algorithms for robot navigation in dynamic environments. The model integrates A* for global path planning, and the Dynamic Window Approach (DWA), fuzzy control, and Q-learning for real-time obstacle avoidance. An improved multi-objective genetic algorithm is used to optimize path length, safety, and smoothness. Experimental results from 50 independent trials demonstrate that the GAA-DFQ algorithm outperforms traditional A* and GAA in path planning, showing the lowest average collision rate (2.6%), the shortest average path length (184 units), and the fastest average computation time (87 seconds), proving its efficiency and stability in complex environments.