In the field of mobile robot path planning, traditional genetic algorithms face issues such as slow convergence, lack of dynamic adaptability, and uncertain mutation directionality. To address these issues, a dichotomy-based multi-step method was used during the population initialization phase to enhance both the quality and diversity of the population. The selection strategy was improved for tournament selection, which reduces the monopolistic dominance of exceptional individuals and preserves population diversity through multiple rounds of grouping competitions. The crossover strategy adopted an adaptive crossover point number, with parents chosen based on a dynamic threshold. The mutation strategy combines a two-layer encoding approach: the first layer uses random mutations to enhance diversity, while the second layer performs goal-oriented mutations, with the selection probability of each layer dynamically adjusted during iterations. Bezier curve optimization of the optimal path was conducted. Experimental results show that the improved genetic algorithm demonstrates significant advantages in terms of path length, smoothness, iteration speed, and computational efficiency.

Path planning for autonomous mobile robots in dynamic environments is a critical challenge, particularly when robots must navigate both known and unknown obstacles in real-time. Traditional methods like ant colony optimization (ACO) have shown promise in global path planning but often suffer from slow convergence and the risk of getting trapped in local optima. Similarly, while the dynamic window approach (DWA) excels in real-time obstacle avoidance, it exhibits delayed response and higher failure rates in scenarios with fast-moving obstacles. To address these challenges, this study proposes a hybrid path planning framework, IACO-QDWA, which integrates an improved ACO (IACO) for global path optimization with a Q-learning-enhanced DWA (QDWA) for dynamic obstacle avoidance. IACO improves global path search through goal-oriented initialization and adaptive pheromone updates, while QDWA enhances local decision-making by optimizing the state-action space. The experiments demonstrate that IACO-QDWA achieves a reasonable balance between path quality, obstacle avoidance success rate, and real-time performance. It exhibits stable obstacle avoidance behavior and reliable navigation capabilities in highly dynamic environments.