UAV path planning method in dynamic environments: an ant colony algorithm-based optimization study

With the rapid development of drone technology, drones are being used more and more widely in fields such as logistics and distribution, agricultural monitoring, disaster relief and environmental protection. However, path planning in dynamic environments remains one of the major challenges for UAVs. Traditional path planning methods such as Dijkstra and A* algorithms perform well in static environments but have limited effectiveness in dynamic environments. This paper proposes an UAV path planning method based on Ant Colony Optimization (ACO) optimization. By introducing a dynamic pheromone update mechanism, environmental prediction technology, and an adaptive parameter adjustment strategy, the adaptability and efficiency of the algorithm in dynamic environments are improved. Simulation experiments have shown that the improved ACO algorithm is superior to the traditional ACO and A* algorithms in terms of path length, calculation time, success rate and path smoothness. The research in this paper provides a feasible solution for the autonomous flight of UAVs in dynamic environments and provides new ideas for the expansion of intelligent optimization algorithms in practical applications.