A Novel Approach to Agoraphilic Path Planning Algorithm with Semantic Terrain Awareness

This research presents a novel approach to the Agoraphilic local path planning algorithm by integrating semantic segmentation for enhanced terrain identification and free space navigation in complex environments. Traditional Agoraphilic algorithms, although effective in free space navigation, often struggle to accurately identify untraversable terrains such as mud or water, mistakenly considering them as navigable in ground robots. By leveraging a self-trained YOLOv8-seg network for semantic segmentation, our method identifies and labels traversable free spaces, such as roads, grass planes, footpaths and sand areas, using image input. This enhancement is applied across the core modules of the traditional Agoraphilic algorithm, resulting in a novel approach for effective navigation across challenging terrain conditions. The proposed semantic segmentation-based free space identification method is experimentally tested in real-world environments, and simulation tests validate the effectiveness of the improved Agoraphilic algorithm. This advancement represents a significant improvement in autonomous robot navigation, particularly in challenging and uneven terrains.