Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach

Chengjia Lei; Shaohua Wu; Yi Yang; Jiayin Xue; Qinyu Zhang

doi:10.1109/ICCC57788.2023.10233604

Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach

Chengjia Lei^*
, Shaohua Wu
, Yi Yang
, Jiayin Xue
, Qinyu Zhang

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In recent years, with the continuous growth of offshore operations, maritime search and rescue (MSAR) has received widespread attention as a crucial guarantee for safety. In this paper, we aim to achieve efficient and communication fault-tolerant MSAR operations by employing trajectory planning and resource scheduling for heterogeneous units involving observation unmanned aerial vehicles (UAVs), router UAVs, and rescue ships equipped with mobile edge computing (MEC). Firstly, we model several essential components related to MSAR, including the ocean current environment, UAVs for observing, fault tolerance of routing network, MEC scheduling, and energy consumption of UAVs. Secondly, we formulate the optimization problem into a decentralized partially observable Markov Decision Process (Dec-POMDP) and then introduce the multi-agent reinforcement learning (MARL) approach to search for an optimal joint strategy. Finally, experimental results demonstrate that in the model of MSAR we constructed, our improved MARL approach, named IPPO-nGAE, outperforms other benchmarks in both efficiency and fault tolerance.

Original language	English
Title of host publication	2023 IEEE/CIC International Conference on Communications in China, ICCC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350345384
DOIs	https://doi.org/10.1109/ICCC57788.2023.10233604
State	Published - 2023
Externally published	Yes
Event	2023 IEEE/CIC International Conference on Communications in China, ICCC 2023 - Dalian, China Duration: 10 Aug 2023 → 12 Aug 2023

Publication series

Name	2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

Conference

Conference	2023 IEEE/CIC International Conference on Communications in China, ICCC 2023
Country/Territory	China
City	Dalian
Period	10/08/23 → 12/08/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Maritime search and rescue
efficiency
fault-tolerant communication
multi-agent reinforcement learning
rescue ship
unmanned aerial vehicle

Access to Document

10.1109/ICCC57788.2023.10233604

Cite this

Lei, C., Wu, S., Yang, Y., Xue, J., & Zhang, Q. (2023). Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach. In 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023 (2023 IEEE/CIC International Conference on Communications in China, ICCC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCC57788.2023.10233604

Lei, Chengjia ; Wu, Shaohua ; Yang, Yi et al. / Maritime Search and Rescue Leveraging Heterogeneous Units : A Multi-Agent Reinforcement Learning Approach. 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE/CIC International Conference on Communications in China, ICCC 2023).

@inproceedings{b14990b090c646989ecc8d2d1abcabee,

title = "Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach",

abstract = "In recent years, with the continuous growth of offshore operations, maritime search and rescue (MSAR) has received widespread attention as a crucial guarantee for safety. In this paper, we aim to achieve efficient and communication fault-tolerant MSAR operations by employing trajectory planning and resource scheduling for heterogeneous units involving observation unmanned aerial vehicles (UAVs), router UAVs, and rescue ships equipped with mobile edge computing (MEC). Firstly, we model several essential components related to MSAR, including the ocean current environment, UAVs for observing, fault tolerance of routing network, MEC scheduling, and energy consumption of UAVs. Secondly, we formulate the optimization problem into a decentralized partially observable Markov Decision Process (Dec-POMDP) and then introduce the multi-agent reinforcement learning (MARL) approach to search for an optimal joint strategy. Finally, experimental results demonstrate that in the model of MSAR we constructed, our improved MARL approach, named IPPO-nGAE, outperforms other benchmarks in both efficiency and fault tolerance.",

keywords = "Maritime search and rescue, efficiency, fault-tolerant communication, multi-agent reinforcement learning, rescue ship, unmanned aerial vehicle",

author = "Chengjia Lei and Shaohua Wu and Yi Yang and Jiayin Xue and Qinyu Zhang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023 ; Conference date: 10-08-2023 Through 12-08-2023",

year = "2023",

doi = "10.1109/ICCC57788.2023.10233604",

language = "英语",

series = "2023 IEEE/CIC International Conference on Communications in China, ICCC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE/CIC International Conference on Communications in China, ICCC 2023",

address = "美国",

}

Lei, C, Wu, S, Yang, Y, Xue, J & Zhang, Q 2023, Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach. in 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023. 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023, Dalian, China, 10/08/23. https://doi.org/10.1109/ICCC57788.2023.10233604

Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach. / Lei, Chengjia; Wu, Shaohua; Yang, Yi et al.
2023 IEEE/CIC International Conference on Communications in China, ICCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE/CIC International Conference on Communications in China, ICCC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Maritime Search and Rescue Leveraging Heterogeneous Units

T2 - 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

AU - Lei, Chengjia

AU - Wu, Shaohua

AU - Yang, Yi

AU - Xue, Jiayin

AU - Zhang, Qinyu

PY - 2023

Y1 - 2023

N2 - In recent years, with the continuous growth of offshore operations, maritime search and rescue (MSAR) has received widespread attention as a crucial guarantee for safety. In this paper, we aim to achieve efficient and communication fault-tolerant MSAR operations by employing trajectory planning and resource scheduling for heterogeneous units involving observation unmanned aerial vehicles (UAVs), router UAVs, and rescue ships equipped with mobile edge computing (MEC). Firstly, we model several essential components related to MSAR, including the ocean current environment, UAVs for observing, fault tolerance of routing network, MEC scheduling, and energy consumption of UAVs. Secondly, we formulate the optimization problem into a decentralized partially observable Markov Decision Process (Dec-POMDP) and then introduce the multi-agent reinforcement learning (MARL) approach to search for an optimal joint strategy. Finally, experimental results demonstrate that in the model of MSAR we constructed, our improved MARL approach, named IPPO-nGAE, outperforms other benchmarks in both efficiency and fault tolerance.

AB - In recent years, with the continuous growth of offshore operations, maritime search and rescue (MSAR) has received widespread attention as a crucial guarantee for safety. In this paper, we aim to achieve efficient and communication fault-tolerant MSAR operations by employing trajectory planning and resource scheduling for heterogeneous units involving observation unmanned aerial vehicles (UAVs), router UAVs, and rescue ships equipped with mobile edge computing (MEC). Firstly, we model several essential components related to MSAR, including the ocean current environment, UAVs for observing, fault tolerance of routing network, MEC scheduling, and energy consumption of UAVs. Secondly, we formulate the optimization problem into a decentralized partially observable Markov Decision Process (Dec-POMDP) and then introduce the multi-agent reinforcement learning (MARL) approach to search for an optimal joint strategy. Finally, experimental results demonstrate that in the model of MSAR we constructed, our improved MARL approach, named IPPO-nGAE, outperforms other benchmarks in both efficiency and fault tolerance.

KW - Maritime search and rescue

KW - efficiency

KW - fault-tolerant communication

KW - multi-agent reinforcement learning

KW - rescue ship

KW - unmanned aerial vehicle

UR - https://www.scopus.com/pages/publications/85172993407

U2 - 10.1109/ICCC57788.2023.10233604

DO - 10.1109/ICCC57788.2023.10233604

M3 - 会议稿件

AN - SCOPUS:85172993407

T3 - 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

BT - 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 10 August 2023 through 12 August 2023

ER -

Lei C, Wu S, Yang Y, Xue J, Zhang Q. Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach. In 2023 IEEE/CIC International Conference on Communications in China, ICCC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE/CIC International Conference on Communications in China, ICCC 2023). doi: 10.1109/ICCC57788.2023.10233604

Maritime Search and Rescue Leveraging Heterogeneous Units: A Multi-Agent Reinforcement Learning Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

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