Time-Constrained UAV Path Planning in 3D Network for Maximum Information Gain

Our focus is on the development of an unmanned aerial vehicle (UAV) path to maximize the collected amount of information from the area of operation (AO) within a specified fuel limit. Different regions of the AO have different monitoring requirements, achieved through visitation at an appropriate altitude. A mixed integer programming (MIP) formulation is developed to solve the path-planning problem with UAV turning radius factored in. Furthermore, a simulated annealing heuristic is developed to solve large problems where the MIP approach is not feasible. Through computational testing we verify that the heuristic generates acceptable results in terms of solution quality (when compared to the exact MIP method) for small to medium size problems, and further verify that it generates results for large problems in acceptable computational time. We empiri-cally test the impact of the fuel limit on the information gained. We explore two methods for increased information gain, using a single UAV with a higher fuel capacity or multiple UAVs with lower fuel capacity. This analysis aims to aid in decision making of the number of UAVs to be deployed and model type selection for military operations.