Multi-Agent Cooperative Search in Multi-Object Uncertain Environment

Peiqiao Shang; Zhihong Peng; Hui He; Lihua Li; Jinqiang Cui

doi:10.1109/ICUS58632.2023.10318355

Multi-Agent Cooperative Search in Multi-Object Uncertain Environment

Peiqiao Shang, Zhihong Peng^*, Hui He, Lihua Li, Jinqiang Cui^*

^*Corresponding author for this work

School of Automation

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

Abstract

Multiagent multi-object search (MAMOS) in uncertain rescue environment has always been considered a challenge, which can be modeled as an Object Oriented-Multiagent Partially Observable Markov Decision Process (OO-MPOMDPs). This paper proposes a Guidance and Belief Synchronization-Object Oriented-Multi-agent Partially Observable Upper Confidence Bound Apply to Tree (GBS-OO-MPOUCT) algorithm which contains two parts. One is belief decomposition, update and synchronization to achieve environment representation, update and multi-agent internal communication, another is deadlock recognition and resolution when robots are getting trapped due to insufficient depth of Monte Carlo tree. We construct many examples to test the performance of the algorithm. The results show that our algorithm can find all objects in fewer steps and observation noise has little impact on algorithm performance.

Original language	English
Title of host publication	Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
Editors	Rong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	160-167
Number of pages	8
ISBN (Electronic)	9798350316308
DOIs	https://doi.org/10.1109/ICUS58632.2023.10318355
Publication status	Published - 2023
Event	2023 IEEE International Conference on Unmanned Systems, ICUS 2023 - Hefei, China Duration: 13 Oct 2023 → 15 Oct 2023

Publication series

Name	Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

Conference

Conference	2023 IEEE International Conference on Unmanned Systems, ICUS 2023
Country/Territory	China
City	Hefei
Period	13/10/23 → 15/10/23

Keywords

POMDPs
belief synchronization
deadlock guidance
object search
uncertain environment

Access to Document

10.1109/ICUS58632.2023.10318355

Cite this

Shang, P., Peng, Z., He, H., Li, L., & Cui, J. (2023). Multi-Agent Cooperative Search in Multi-Object Uncertain Environment. In R. Song (Ed.), Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023 (pp. 160-167). (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS58632.2023.10318355

Shang, Peiqiao ; Peng, Zhihong ; He, Hui et al. / Multi-Agent Cooperative Search in Multi-Object Uncertain Environment. Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. editor / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 160-167 (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023).

@inproceedings{d9c3b565c4d944baa711ac6902f18f23,

title = "Multi-Agent Cooperative Search in Multi-Object Uncertain Environment",

abstract = "Multiagent multi-object search (MAMOS) in uncertain rescue environment has always been considered a challenge, which can be modeled as an Object Oriented-Multiagent Partially Observable Markov Decision Process (OO-MPOMDPs). This paper proposes a Guidance and Belief Synchronization-Object Oriented-Multi-agent Partially Observable Upper Confidence Bound Apply to Tree (GBS-OO-MPOUCT) algorithm which contains two parts. One is belief decomposition, update and synchronization to achieve environment representation, update and multi-agent internal communication, another is deadlock recognition and resolution when robots are getting trapped due to insufficient depth of Monte Carlo tree. We construct many examples to test the performance of the algorithm. The results show that our algorithm can find all objects in fewer steps and observation noise has little impact on algorithm performance.",

keywords = "POMDPs, belief synchronization, deadlock guidance, object search, uncertain environment",

author = "Peiqiao Shang and Zhihong Peng and Hui He and Lihua Li and Jinqiang Cui",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Unmanned Systems, ICUS 2023 ; Conference date: 13-10-2023 Through 15-10-2023",

year = "2023",

doi = "10.1109/ICUS58632.2023.10318355",

language = "English",

series = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",

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

pages = "160--167",

editor = "Rong Song",

booktitle = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",

address = "United States",

}

Shang, P, Peng, Z, He, H, Li, L & Cui, J 2023, Multi-Agent Cooperative Search in Multi-Object Uncertain Environment. in R Song (ed.), Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023, Institute of Electrical and Electronics Engineers Inc., pp. 160-167, 2023 IEEE International Conference on Unmanned Systems, ICUS 2023, Hefei, China, 13/10/23. https://doi.org/10.1109/ICUS58632.2023.10318355

Multi-Agent Cooperative Search in Multi-Object Uncertain Environment. / Shang, Peiqiao; Peng, Zhihong; He, Hui et al.
Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. ed. / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 160-167 (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023).

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

TY - GEN

T1 - Multi-Agent Cooperative Search in Multi-Object Uncertain Environment

AU - Shang, Peiqiao

AU - Peng, Zhihong

AU - He, Hui

AU - Li, Lihua

AU - Cui, Jinqiang

PY - 2023

Y1 - 2023

N2 - Multiagent multi-object search (MAMOS) in uncertain rescue environment has always been considered a challenge, which can be modeled as an Object Oriented-Multiagent Partially Observable Markov Decision Process (OO-MPOMDPs). This paper proposes a Guidance and Belief Synchronization-Object Oriented-Multi-agent Partially Observable Upper Confidence Bound Apply to Tree (GBS-OO-MPOUCT) algorithm which contains two parts. One is belief decomposition, update and synchronization to achieve environment representation, update and multi-agent internal communication, another is deadlock recognition and resolution when robots are getting trapped due to insufficient depth of Monte Carlo tree. We construct many examples to test the performance of the algorithm. The results show that our algorithm can find all objects in fewer steps and observation noise has little impact on algorithm performance.

AB - Multiagent multi-object search (MAMOS) in uncertain rescue environment has always been considered a challenge, which can be modeled as an Object Oriented-Multiagent Partially Observable Markov Decision Process (OO-MPOMDPs). This paper proposes a Guidance and Belief Synchronization-Object Oriented-Multi-agent Partially Observable Upper Confidence Bound Apply to Tree (GBS-OO-MPOUCT) algorithm which contains two parts. One is belief decomposition, update and synchronization to achieve environment representation, update and multi-agent internal communication, another is deadlock recognition and resolution when robots are getting trapped due to insufficient depth of Monte Carlo tree. We construct many examples to test the performance of the algorithm. The results show that our algorithm can find all objects in fewer steps and observation noise has little impact on algorithm performance.

KW - POMDPs

KW - belief synchronization

KW - deadlock guidance

KW - object search

KW - uncertain environment

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DO - 10.1109/ICUS58632.2023.10318355

M3 - Conference contribution

AN - SCOPUS:85180124412

T3 - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

SP - 160

EP - 167

BT - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 October 2023 through 15 October 2023

ER -

Shang P, Peng Z, He H, Li L, Cui J. Multi-Agent Cooperative Search in Multi-Object Uncertain Environment. In Song R, editor, Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 160-167. (Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023). doi: 10.1109/ICUS58632.2023.10318355

Multi-Agent Cooperative Search in Multi-Object Uncertain Environment

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