Abstract
The persistent surveillance problem has been proved to be an NP hard problem for multiple unmanned aerial vehicle systems (UAVs). However, most studies in multiple UAV control focus on control cooperative path planning in a single swarm, while dynamic deployment of a multiswarm system is neglected. This paper proposes a collective control scheme to drive a multiswarm UAVs system to spread out over a time-sensible environment to provide persistent adaptive sensor coverage in event-related surveillance scenarios. We design the digital turf model to approximate the mixture information of mission requirements and surveillance reward. Moreover, we design a data clustering-based algorithm for the dynamic assignment of UAV swarms, which can promote workload balance, while also allowing real-time response to emergencies. Finally, we evaluate the proposed architecture by means of simulation and find that our method is superior to the conventional control strategy in terms of detection efficiency and subswarm equilibrium degree.
Highlights
We present a distributed digital turf model (DTM) to describe the updating process of persistent surveillance
To automatically set the deployment and configuration of unmanned aerial vehicle systems (UAVs) swarms, a cluster-based control scheme is used on the basis of the proposed DTM map. e main contributions of our proposed method are listed as follows
We investigate the problem of multiswarm UAV surveillance and provide a two-layer collective control scheme for the deployment of UAV swarms and the number of UAVs in each swarm
Summary
Multi-UAV searching has always been a primary problem in multi-UAV cooperation theory, and its extension to persistent surveillance presents greater challenges. ere has been a rising interest in multi-UAV persistent surveillance as a result of emerging demands of real-time continuous information gathering. Multi-UAV searching has always been a primary problem in multi-UAV cooperation theory, and its extension to persistent surveillance presents greater challenges. Ere has been a rising interest in multi-UAV persistent surveillance as a result of emerging demands of real-time continuous information gathering. With the improvement in UAVs conducting 24/7 persistent missions and the growing demands of realtime continuous information of the environment, the multi-UAV persistent surveillance task has been studied in many research studies. E persistent surveillance of single UAVs has been studied extensively, with typical searching methods including optimization such as spanning tree coverage [13], dynamic programming [14], area partition [15], potential field methods [16], and other swarming approaches [17, 18]
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