Online Unmanned Aerial Vehicles Search Planning in an Unknown Search Environment

Abstract

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.