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In modern military and civilian fields，UAV path planning in complex electromagnetic environments encounters severe challenges，such as dynamic distribution of threat areas，difficulty in balancing safety and efficiency with traditional algorithms，and insufficient accuracy in three-dimensional spatial modeling. To address these issues，an improved A* path planning algorithm based on electromagnetic grid was proposed. Relying on the GeoSOT-3D framework，the electromagnetic grid conducts multi-dimensional and refined modeling of the electromagnetic environment，supports the dynamic fusion of multi-source information，and provides high-precision environmental representation for path planning. On the basis of the traditional A* algorithm，this algorithm introduces a probability factor to reconstruct the loss function，quantifies the threat cost using radar detection probability，and combines 3D diagonal distance to optimize heuristic search，thereby achieving a dynamic balance among path length，safety，and computational efficiency. Experimental results show that compared with the traditional A* algorithm，the proposed method reduces the number of threat area grids by 46.94% and improves computational efficiency by 36.97%; it also supports adaptation to different task requirements by adjusting the danger coefficient. This study provides a highly robust path planning solution for UAV combat missions in complex electromagnetic environments.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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