Abstract
In this paper, we consider the navigation of a group of solar-powered unmanned aerial vehicles (UAVs) for periodical monitoring of a set of mobile ground targets in urban environments. We consider the scenario where the number of targets is larger than that of the UAVs, and the targets spread in the environment, so that the UAVs need to carry out a periodical surveillance. The existence of tall buildings in urban environments brings new challenges to the periodical surveillance mission. They may not only block the Line-of-Sight (LoS) between a UAV and a target, but also create some shadow region, so that the surveillance may become invalid, and the UAV may not be able to harvest energy from the sun. The periodical surveillance problem is formulated as an optimization problem to minimize the target revisit time while accounting for the impact of the urban environment. A nearest neighbour based navigation method is proposed to guide the movements of the UAVs. Moreover, we adopt a partitioning scheme to group targets for the purpose of narrowing UAVs’ moving space, which further reduces the target revisit time. The effectiveness of the proposed method is verified via computer simulations.
Highlights
Unmanned aerial vehicles (UAVs) have found numerous applications in both military and civilian domains
We present a path planning method that is based on the Rapidly-exploring Random Tree (RRT)
This paper focuses on the scenario where the number of unmanned aerial vehicles (UAVs) is not enough to persistently monitor the targets, so a periodical surveillance is conducted by the UAVs
Summary
Unmanned aerial vehicles (UAVs) have found numerous applications in both military and civilian domains. We present a path planning method that is based on the Rapidly-exploring Random Tree (RRT). This method can quickly find a feasible path for the UAV to intercept the target in the scenario where the target moves along a known trajectory. The proposed autonomous navigation algorithm that navigates a UAV team in order to periodically survey a group of mobile ground targets is the main contribution of this paper. It is computationally efficient and implementable online, since it is a randomized RRT-based approach.
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