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Sensors (Basel). 2019 Mar 30;19(7). pii: E1545. doi: 10.3390/s19071545.

Profit-Driven Adaptive Moving Targets Search with UAV Swarms.

Author information

1
Shenzhen Key Lab of Information Theory & Future Network Arch, Peking University Shenzhen Graduate School, Shenzhen 518055, China. lixianfeng.sz@pku.edu.cn.
2
Shenzhen Key Lab of Information Theory & Future Network Arch, Peking University Shenzhen Graduate School, Shenzhen 518055, China. chen-jie@pku.edu.cn.
3
Shenzhen Key Lab of Information Theory & Future Network Arch, Peking University Shenzhen Graduate School, Shenzhen 518055, China. dengfan@pku.edu.cn.
4
Shenzhen Key Lab of Information Theory & Future Network Arch, Peking University Shenzhen Graduate School, Shenzhen 518055, China. lih64@pkusz.edu.cn.
5
Future Network PKU Lab of National Major Research Infrastructure, Peking University Shenzhen Graduate School, Shenzhen 518055, China. lih64@pkusz.edu.cn.

Abstract

This paper presents a novel distributed algorithm for a moving targets search with a team of cooperative unmanned aerial vehicles (UAVs). UAVs sense targets using on-board sensors and the information can be shared with teammates within a communication range. Based on local and shared information, the UAV swarm tries to maximize its average observation rate on targets. Unlike traditional approaches that treat the impact from different sources separately, our framework characterizes the impact of moving targets and collaborating UAVs on the moving decision for each UAV with a unified metric called observation profit. Based on this metric, we develop a profit-driven adaptive moving targets search algorithm for a swarm of UAVs. The simulation results validate the effectiveness of our framework in terms of both observation rate and its adaptiveness.

KEYWORDS:

moving targets search; observation profit; unmanned aerial vehicle (UAV)

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