Ant colony optimization-based path planning for UAV navigation in dynamic environments

In a dynamic environment, path planning for Unmanned Aerial Vehicles (UAVs) is challenging due to obstacles and changing conditions. To solve this, bio-inspired metaheuristic algorithms such as Ant Colony Optimization (ACO) have shown promising result in solving optimization problems. In this paper we have presented ACO-based path planning algorithms for UAV navigation in dynamic environments. We determined the optimal paths for UAVs based on swarm intelligence and ant behavior. Pheromone trails and attractive heuristics help the UAVs navigate safely and efficiently, adapting to their environment dynamically. The results of our simulation analysis show that the proposed algorithm is both effective and efficient for a dynamic environment. With its ability to handle a variety of dynamic scenarios, ACO is ideal for UAV applications like surveillance, delivery, and search and rescue missions. In the simulation environment we have simulated 5 different scenarios on the grid and the results show that the algorithm is able find the optimum path efficiently in the dynamic environment with the obstacles. We plan to improve accuracy with a hybrid approach in the future.