Unmanned Aerial Vehicle route optimization using ant system algorithm

Unmanned Aerial Vehicle (UAV) is defined as aircraft without the onboard presence of pilots. UAVs have been used to perform intelligence, surveillance, and reconnaissance missions. The UAVs are not limited to military operations, they can also be used in commercial applications such as telecommunications, ground traffic control, search and rescue operations, crop monitoring, etc. In this paper, we propose a swarm intelligence-based method for UAVs´ route optimization. The team of UAVs is used for area coverage with the defined set of waypoints. The problem can be interpreted as a well-known Traveling Salesman Problem where the task is to find the route of minimal length such that all the waypoints are visited only once. We applied the Ant System algorithm and compared it with the Nearest Neighbor Search. The experimental results confirm the effectiveness of our method, especially for a large number of waypoints.