Uav Route Planning For Maximum Target Coverage
This addresses the practical optimization challenge of efficiently tasking expensive UAVs for monitoring targets in military and civil operations, but it is incremental as it modifies an existing algorithm.
The paper tackled the problem of planning flight routes for a limited number of UAVs to maximize target coverage within flight range constraints, and the result showed that the proposed Max-Min Ant System method increased the number of covered targets compared to a Nearest Neighbour heuristic.
Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil operations is getting popular. One of the challenges in effectively tasking these expensive vehicles is planning the flight routes to monitor the targets. In this work, we aim to develop an algorithm which produces routing plans for a limited number of UAVs to cover maximum number of targets considering their flight range. The proposed solution for this practical optimization problem is designed by modifying the Max-Min Ant System (MMAS) algorithm. To evaluate the success of the proposed method, an alternative approach, based on the Nearest Neighbour (NN) heuristic, has been developed as well. The results showed the success of the proposed MMAS method by increasing the number of covered targets compared to the solution based on the NN heuristic.