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Recent years have seen a surge in service robots built on the ROS framework, where effective path planning algorithms are crucial for their successful operation. However, real-world operation presents challenges due to changes in environmental features and unexpected dynamic obstacles. If the path planning algorithms fail to update the state of the map layers in a timely manner, it can result in mission failures for the robots. Therefore, this study proposes a novel Motion Trajectory Analysis Regulator Navigation Framework within the ROS system. This framework incorporates path planning regulators for both global and local path planning algorithms, effectively mitigating path planning failures caused by dynamic environmental changes. For enhanced flexibility in path planning and dynamic obstacle avoidance, this framework proposes the ADRRT* algorithm for global path planning and the FGM-DWA algorithm for local path planning, replacing the traditional A* and DWA algorithms, respectively. Real-field experiments with a service robot demonstrate that the proposed methods not only ensure successful navigation with 100% success rate and a 23% improvement in time efficiency but also effectively handle dynamic and U-shaped obstacles, overcoming the challenge of path planning failures.
Chen et al. (Mon,) studied this question.
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