ドローン物流のための改良型A-Starアルゴリズムを用いた長距離経路計画

抄録

In recent years, advancements in UAV and drone technologies have expanded their use in areas like photography and inspection, with drone delivery experiments gaining attention in Japan. The Japanese government is promoting drone logistics as part of its Next-Generation Air Mobility Initiative, however, practical implementation requires automatic flight path generation. This study proposes an efficient path planning method using an improved A-Star algorithm, incorporating obstacle and wind condition data from the 4D Spatio-Temporal Data Exchange Infrastructure (4D-STDEI). The algorithm minimizes a cost function, adding wind condition costs to account for wind effects. Also, in open areas with fewer obstacles, the algorithm reduces node granularity (or the voxel’s “zoom level”) to lower computational costs, making large-scale pathfinding more efficient. The paper presents simulation results comparing the improved A-Star algorithm with and without wind condition costs, showing that the proposed method consistently reduced maximum wind speeds and improved computation times in larger search areas. This approach enables faster and safer drone flight path exploration.