A Novel Real-Time Path Planning Method for UAV Based on Fuzzy Logic Sparse A* Algorithm

In the context of target tracking tasks, this study addresses the challenges of target inaccessibility and hovering around the target for UAVs by proposing an improved sparse A* algorithm enhanced with fuzzy logic. The algorithm’s heuristic function is meticulously designed to ensure the safety and effectiveness of the target tracking task. A fuzzy optimizer is employed to dynamically adjust the planning step size, thereby balancing the algorithm’s efficiency and optimality. Furthermore, by incorporating a variable simulation step, the UAV’s speed variations are integrated into the sparse A* algorithm, which not only smooths the flight path but also enhances the dynamic target tracking performance. The simulation results validate the effectiveness of the improved algorithm, demonstrating significant improvements in both path smoothness and tracking accuracy. This approach ensures that UAVs can effectively and safely track moving targets, overcoming common challenges such as target inaccessibility and hovering around the target. The proposed method optimizes overall mission performance, providing a robust solution for dynamic target tracking in complex environments. The findings of this study contribute to the advancement of UAV navigation and control systems, offering practical implications for real-world applications in surveillance, reconnaissance, and other related fields.