A Dual-Layer Hybrid-A* Path Planning Algorithm for Unstructured Environments Based on Phase Windows

In mobile robotics, path planning enables autonomous navigation to specified destinations. However, complex terrain can lead to excessive tilting or even overturning, compromising stability and safety. Traditional path-planning algorithms often fail to fully account for dynamic terrain variations and robot motion constraints. To address these limitations, this paper proposes the novel dual-layer Hybrid-A* algorithm, enhanced with dynamic phase windows. This approach represents a significant innovation by integrating real-time feedback mechanisms and adaptive adjustments to phase windows, enabling continuous path refinement in response to both environmental changes and robot motion limitations. The guidance layer introduces a bicubic interpolation-based super-resolution technique to refine elevation maps, offering more accurate posture estimation. In the planning layer, we propose the dynamic use of multiple cost functions, an adaptive expansion radius, pruning strategies, and a phase-window activation mechanism, effectively addressing the computational challenges posed by large search spaces. The integration of these strategies allows the algorithm to outperform traditional methods, particularly in unstructured environments with complex terrain. Experimental results demonstrate the effectiveness of the proposed method in generating optimized paths that satisfy robot motion constraints, ensuring both efficiency and safety in real-world applications.