面向无人机路径规划的A*-DWA分层融合算法OA

To address the issues of low search efficiency,poor path smoothness,and limited local obstacle avoidance in traditional A* algorithms within complex 3D environments,this paper proposed a hierarchical fusion algorithm combining A* and DWA.The algorithm first extended the A*algorithm into 3D space using a 26-neighbor node search strategy.It introduced a dynamic adjustment term into the cost evaluation function to achieve adaptive weighting,and employed the Douglas-Peucker algorithm combined with cubic B-spline curves to smooth paths.Second,it integrated a 3D-extended DWA algorithm to compensate for A*'s local obstacle avoidance limitations.A 3D dynamic window model was constructed via kinematic decoupling,and a cosine similarity measure was introduced to enhance the evaluation function,thereby improving real-time obstacle avoidance performance.Finally,a dynamic feedback mechanism was designed to enable adaptive correction of the global path,forming a closed-loop optimization system:"A* global planning-DWA local obstacle avoidance-Dynamic feedback."Simulation results demonstrated that in both static and dynamic 3D environments,the A*-DWA hierarchical fusion algorithm significantly outperformed other comparison algorithms in path length,planning time,and path smoothness.Obstacle avoidance success rates exceeded 90%across multiple scenarios,validating the effectiveness of the A*-DWA hierarchical fusion algorithm.