A Two-stage Framework for Online Unmanned Aerial Vehicles Search Planning

Unmanned Aerial Vehicles (UAV), also known as drones, have been widely used in regional data collection and information search, but there are also many practical challenges. In real-world operations of UAV search, the payoff and cost at each search point are unknown for the planner in advance which poses a great challenge to decision making. To this end, we first propose the problem of online decision making in UAV search planning where the drone has limited energy supply as a constraints and has to make an irrevocable decision to search this area or route to the next in an online manner. Then the online UAV search planning problem is decoupled into a traveling salesman problem (TSP) and an online resource planning problem such that it can be solved in a two-stage procedure. Specifically, the routing of search is obtained by solving TSP based on ant colony optimization, and the online decision is made through an online linear programming which is proven to be near-optimal. The effectiveness of the proposed two-stage approach is validated in wide-applied dataset, and experimental results show the superior performance of online search decision making.