Improved dynamic window approach for UAV local path planning in multi-dynamic obstacles environments using sparrow search algorithm

The traditional Dynamic Window Approach (DWA) for local path planning of unmanned aerial vehicles (UAVs) exhibits limitations in flexibility and robustness. Specifically, its fixed evaluation function weights, velocity sampling resolution, and dynamic window ranges fail to adapt to changing environmental conditions, resulting in reduced adaptability of the velocity search space. To address this issue, this study proposes an improved DWA based on the Sparrow Search Algorithm (SSA). First, the proposed algorithm adaptively adjusts dynamic window parameters according to the complexity of the obstacle environment, thereby optimizing the UAV's velocity search space. Second, a velocity sampling resolution strategy is introduced to achieve an effective trade-off between the quality and quantity of predicted trajectories based on the density of dynamic obstacles. Third, by leveraging the strong global search capability and rapid convergence properties of the SSA, the weights of the evaluation function are adaptively optimized to enhance global optimality. Experimental results show that, compared with DWA in a dense multiple dynamic-static obstacles scenario, the proposed algorithm achieves improvements of 8.8%, 66.7%, and 18% in path length, safety distance, and number of iterations, respectively. These enhancements contribute to improved planning efficiency, safety, and overall optimality in UAV operations.