A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm

There is tremendous demand for marine environmental observation, which requires the development of a multi-agent cooperative observation algorithm to guide Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) to observe isotherm data of the mesoscale vortex. The task include two step...

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Autores principales:	Kai Hu, Xu Chen, Qingfeng Xia, Junlan Jin, Liguo Weng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sea and air observation multi-agent collaboration data-driven deep reinforcement learning Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/a78d7bf048994b6d98b0403c2049b875
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spelling	oai:doaj.org-article:a78d7bf048994b6d98b0403c2049b8752021-11-25T18:04:01ZA Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm10.3390/jmse91111892077-1312https://doaj.org/article/a78d7bf048994b6d98b0403c2049b8752021-10-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1189https://doaj.org/toc/2077-1312There is tremendous demand for marine environmental observation, which requires the development of a multi-agent cooperative observation algorithm to guide Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) to observe isotherm data of the mesoscale vortex. The task include two steps: firstly, USVs search out the isotherm, navigate independently along the isotherm, and collect marine data; secondly, a UAV takes off, and in its one round trip, the UAV and USVs jointly perform the task of the UAV reading the observation data from USVs. In this paper, aiming at the first problem of the USV following the isotherm in an unknown environment, a data-driven Deep Deterministic Policy Gradient (DDPG) control algorithm is designed that allows USVs to navigate independently along isotherms in unknown environments. In addition, a hybrid cooperative control algorithm based on a multi-agent DDPG is adopted to solve the second problem, which enables USVs and a UAV to complete data reading tasks with the shortest flight distance of the UAV. The experimental simulation results show that the trained system can complete this tas, with good stability and accuracy.Kai HuXu ChenQingfeng XiaJunlan JinLiguo WengMDPI AGarticlesea and air observationmulti-agent collaborationdata-drivendeep reinforcement learningNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1189, p 1189 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sea and air observation multi-agent collaboration data-driven deep reinforcement learning Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	sea and air observation multi-agent collaboration data-driven deep reinforcement learning Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Kai Hu Xu Chen Qingfeng Xia Junlan Jin Liguo Weng A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm
description	There is tremendous demand for marine environmental observation, which requires the development of a multi-agent cooperative observation algorithm to guide Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) to observe isotherm data of the mesoscale vortex. The task include two steps: firstly, USVs search out the isotherm, navigate independently along the isotherm, and collect marine data; secondly, a UAV takes off, and in its one round trip, the UAV and USVs jointly perform the task of the UAV reading the observation data from USVs. In this paper, aiming at the first problem of the USV following the isotherm in an unknown environment, a data-driven Deep Deterministic Policy Gradient (DDPG) control algorithm is designed that allows USVs to navigate independently along isotherms in unknown environments. In addition, a hybrid cooperative control algorithm based on a multi-agent DDPG is adopted to solve the second problem, which enables USVs and a UAV to complete data reading tasks with the shortest flight distance of the UAV. The experimental simulation results show that the trained system can complete this tas, with good stability and accuracy.
format	article
author	Kai Hu Xu Chen Qingfeng Xia Junlan Jin Liguo Weng
author_facet	Kai Hu Xu Chen Qingfeng Xia Junlan Jin Liguo Weng
author_sort	Kai Hu
title	A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm
title_short	A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm
title_full	A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm
title_fullStr	A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm
title_full_unstemmed	A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm
title_sort	control algorithm for sea–air cooperative observation tasks based on a data-driven algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a78d7bf048994b6d98b0403c2049b875
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A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm

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