Energy-efficient decentralized navigation of a team of solar-powered UAVs for collaborative eavesdropping on a mobile ground target in urban environments

Abstract

This paper considers using a team of solar-powered UAVs to collaboratively eavesdrop on a mobile ground target in an urban environment. A practical application is that the police department uses UAVs to collect the transmitted data from a criminal suspect so that the transmitted content can be understood. Using UAVs in this task is a much more cost-effective and also less suspicious option than the conventional surveillance by a police helicopter. We focus on the online and decentralized path planning for the solar-powered UAV team. We formulate a multi-objective optimization problem which forces the UAVs to complete the uninterrupted eavesdropping task, not enter any No-fly space (NFS), keep a safe distance from each other and harvest as much energy as possible. We propose a Rapidly-exploring Random Tree (RRT) based path planning method, consisting of an initial planning phase and an online planning phase. In the online planning phase, each UAV plans its own path based on the shared information across the team. Since the UAV model can be taken into account in the planning phase, the obtained UAV paths are feasible without any later adjustment. More importantly, this method is easily implementable in real-time.