Enhanced A* Algorithm For Global Path Planning

Abstract

Global path planning is a fundamental component of autonomous navigation systems, enabling robots and vehicles to traverse complex environments efficiently and safely. While the traditional A* algorithm is widely used for its optimality and completeness in static environments, it suffers from several limitations, including suboptimal path smoothness, insufficient safety margins, and lack of adaptability to varying obstacle densities. To address these challenges, we propose an Enhanced A* Algorithm for Safe and Smooth Navigation, which incorporates dynamic heuristic weighting, obstacle-aware safety mechanisms, and B-spline-based path smoothing. These enhancements enable the generation of globally optimal paths that are both safe and smooth, making the algorithm suitable for real-world navigation scenarios.