Unmanned Aerial Vehicles (UAVs) have been widely used in localized data collection and information search. However, there are still many practical challenges in real-world operations of UAV search, such as unknown search environments. Specifically, the payoff and cost at each search point are unknown for the planner in advance, which poses a great challenge to decision making. That is, UAV search decisions should be made sequentially in an online manner thereby adapting to the unknown search environment. To this end, this paper initiates the problem of online decision making in UAV search planning, where the drone has limited energy supply as a constraint and has to make an irrevocable decision to search this area or route to the next in an online manner. To overcome the challenge of unknown search environment, a joint-planning approach is proposed, where both route selection and search decision are made in an integrated online manner. The integrated online decision is made through an online linear programming which is proved to be near-optimal, resulting in high information search revenue. Furthermore, this joint-planning approach can be favorably applied to multi-round online UAV search planning scenarios, showing a great superiority in first-mover dominance of gathering information. The effectiveness of the proposed approach is validated in a widely applied dataset, and experimental results show the superior performance of online search decision making.

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.