Path Planning of UAV Formations Based on Semantic Maps

This paper primarily studies the path planning problem for UAV formations guided by semantic map information. Our aim is to integrate prior information from semantic maps to provide initial information on task points for UAV formations, thereby planning formation paths that meet practical requirements. Firstly, a semantic segmentation network model based on multi-scale feature extraction and fusion is employed to obtain UAV aerial semantic maps containing environmental information. Secondly, based on the semantic maps, a three-point optimization model for the optimal UAV trajectory is established, and a general formula for calculating the heading angle is proposed to approximately decouple the triangular equation of the optimal trajectory. For large-scale formations and task points, an improved fuzzy clustering algorithm is proposed to classify task points that meet distance constraints by clusters, thereby reducing the computational scale of single samples without changing the sample size and improving the allocation efficiency of the UAV formation path planning model. Experimental data show that the UAV cluster path planning method using angle-optimized fuzzy clustering achieves an 8.6% improvement in total flight range compared to other algorithms and a 17.4% reduction in the number of large-angle turns.