A Simulation Analysis of Optimized Ant Colony Algorithm for UAV Combat Path

Addressing issues with traditional ant colony algorithms in UAV trajectory planning, such as frequent turns and slow or non-convergence in expansive multi-dimensional paths, we innovatively propose an improved version of the ant colony algorithm. This method utilizes map-based construction of a three-dimensional space model and employs spatial slicing techniques to prevent excessive grid region traversal when searching for optimal paths. Additionally, we introduce mechanisms for updating the optimal path information elements per generation and distance heuristic strategies, significantly enhancing the algorithm's convergence speed and efficiency. Experimental results demonstrate that compared to traditional ant colony algorithms and Rapidly-exploring Random Tree (RRT) algorithms, the improved ant colony algorithm achieves a 64.5% and 16.5% increase in search efficiency, respectively, reduces average turning angles by 49% and 62.6%, and shortens trajectory lengths by 39.6% compared to traditional ant colony algorithms. This research on the improved ant colony algorithm provides an efficient and feasible solution for emergency rescue path planning for UAVs.