Dynamic Path Planning via Enhanced ACO and DWA Algorithms

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.