Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms

In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task All...

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Main Authors: Krishna Arjun, David Parlevliet, Hai Wang, Amirmehdi Yazdani
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Robotics
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Online Access:https://www.mdpi.com/2218-6581/14/7/93
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author Krishna Arjun
David Parlevliet
Hai Wang
Amirmehdi Yazdani
author_facet Krishna Arjun
David Parlevliet
Hai Wang
Amirmehdi Yazdani
author_sort Krishna Arjun
collection DOAJ
description In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stands out as one of the (Nondeterministic Polynomial) NP-hard problems. CF pertains to the effective coordination and grouping of agents or robots for efficient task execution, achieved through optimal task allocation. In this context, this paper delivers a succinct overview of dynamic task allocation and CF strategies. It conducts a comprehensive examination of diverse strategies employed for MRTA. The analysis encompasses the advantages, disadvantages, and comparative assessments of these strategies with a focus on CF. Furthermore, this study introduces a novel classification system for prominent task allocation methods and compares these methods with simulation analysis. The fidelity and effectiveness of the proposed CF approach are substantiated through comparative assessments and simulation studies.
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spelling doaj-art-e2fa43bc79f04b6fb3ee1288c59f38562025-08-20T03:32:27ZengMDPI AGRobotics2218-65812025-07-011479310.3390/robotics14070093Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and MechanismsKrishna Arjun0David Parlevliet1Hai Wang2Amirmehdi Yazdani3School of Engineering and Energy, Murdoch University, Perth, WA 6150, AustraliaSchool of Engineering and Energy, Murdoch University, Perth, WA 6150, AustraliaSchool of Engineering and Energy, Murdoch University, Perth, WA 6150, AustraliaSchool of Engineering and Energy, Murdoch University, Perth, WA 6150, AustraliaIn practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stands out as one of the (Nondeterministic Polynomial) NP-hard problems. CF pertains to the effective coordination and grouping of agents or robots for efficient task execution, achieved through optimal task allocation. In this context, this paper delivers a succinct overview of dynamic task allocation and CF strategies. It conducts a comprehensive examination of diverse strategies employed for MRTA. The analysis encompasses the advantages, disadvantages, and comparative assessments of these strategies with a focus on CF. Furthermore, this study introduces a novel classification system for prominent task allocation methods and compares these methods with simulation analysis. The fidelity and effectiveness of the proposed CF approach are substantiated through comparative assessments and simulation studies.https://www.mdpi.com/2218-6581/14/7/93multi-robotmulti-robot task allocationcoalition formationreinforcement learningconvergenceevolutionary optimization
spellingShingle Krishna Arjun
David Parlevliet
Hai Wang
Amirmehdi Yazdani
Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms
Robotics
multi-robot
multi-robot task allocation
coalition formation
reinforcement learning
convergence
evolutionary optimization
title Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms
title_full Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms
title_fullStr Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms
title_full_unstemmed Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms
title_short Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms
title_sort optimizing coalition formation strategies for scalable multi robot task allocation a comprehensive survey of methods and mechanisms
topic multi-robot
multi-robot task allocation
coalition formation
reinforcement learning
convergence
evolutionary optimization
url https://www.mdpi.com/2218-6581/14/7/93
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AT haiwang optimizingcoalitionformationstrategiesforscalablemultirobottaskallocationacomprehensivesurveyofmethodsandmechanisms
AT amirmehdiyazdani optimizingcoalitionformationstrategiesforscalablemultirobottaskallocationacomprehensivesurveyofmethodsandmechanisms