Dynamic Obstacle Avoidance and Path Planning for Mobile Robots Integrating Improved Rapidly-Exploring Random Tree-Star and Improved Dynamic Window Approach

With the application and popularization of artificial intelligence and intelligent robots in daily life, the autonomous navigation and flexible operation capabilities of mobile robots have become particularly critical. Mobile robots perform well in regular environments, but face problems such as low accuracy in dynamic obstacle avoidance and weak adaptability to complex terrains. This study proposes to enhance the adaptability of the Rapidly-exploring Random Tree Star algorithm and integrate it with the A-Star algorithm, the Dynamic Window Approach, and visual sensor to construct an obstacle avoidance model. The objective is to enable the improved model to recognize various terrain features and enhance the accuracy of the path planning algorithm. The proposed model performed well in obstacle avoidance, with a success rate of 95.78% after ten training epochs and no more than four collisions within 4 minutes. In the experiment, as the obstacle increased every minute, the response speed of the proposed model remained below 25 seconds. The above results indicate that the quality of the planned path is higher than that of the other three models. The path optimization improvement combined with the A* algorithm is effective and has high real-time and accuracy, which can make mobile robots widely used in industries such as services, navigation, and logistics.