Flocks formation model for self-interested UAVs

Azoulay, Rina; Reches, Shulamit

doi:10.1007/s11370-021-00354-x

“…Azoulay and Reches [12], [13] developed a multi-agent (MA) blackboard-based protocol to form clusters of selfinterested UAVs with close routes. According to the proposed protocol, the formed clusters are saved in a centralized blackboard, and any new UAV can either join an existing flock or create a new one and publish it in the centralized blackboard.…”

Section: A Bio-inspired Algorithmsmentioning

confidence: 99%

Machine Learning Methods for UAV Flocks Management-A Survey

Azoulay¹,

Haddad

²

,

Reches

³

2021

Self Cite

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The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.

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“…Azoulay and Reches [12,13] developed a multi-agent (MA) blackboard-based protocol to form clusters of self-interested UAVs with close routes. According to the proposed protocol, the formed clusters are saved in a centralized blackboard, and any new UAV can either join an existing flock or create a new one and publish it in the centralized blackboard.…”

Section: Bio-inspired Clustering Algorithmsmentioning

confidence: 99%

Machine Learning Methods for Management UAV Flocks -- a Survey

Azoulay,

Haddad,

Reches

2021

Preprint

Self Cite

0

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The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.

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A flocking control algorithm of multi-agent systems based on cohesion of the potential function

Li,

Yang,

Jiang

et al. 2023

Complex Intell. Syst.

0

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Flocking cohesion is critical for maintaining a group’s aggregation and integrity. Designing a potential function to maintain flocking cohesion unaffected by social distance is challenging due to the uncertainty of real-world conditions and environments that cause changes in agents’ social distance. Previous flocking research based on potential functions has primarily focused on agents’ same social distance and the attraction–repulsion of the potential function, ignoring another property affecting flocking cohesion: well depth, as well as the effect of changes in agents’ social distance on well depth. This paper investigates the effect of potential function well depths and agent’s social distances on the multi-agent flocking cohesion. Through the analysis, proofs, and classification of these potential functions, we have found that the potential function well depth is proportional to the flocking cohesion. Moreover, we observe that the potential function well depth varies with the agents’ social distance changes. Therefore, we design a segmentation potential function and combine it with the flocking control algorithm in this paper. It enhances flocking cohesion significantly and has good robustness to ensure the flocking cohesion is unaffected by variations in the agents’ social distance. Meanwhile, it reduces the time required for flocking formation. Subsequently, the Lyapunov theorem and the LaSalle invariance principle prove the stability and convergence of the proposed control algorithm. Finally, this paper adopts two subgroups with different potential function well depths and social distances to encounter for simulation verification. The corresponding simulation results demonstrate and verify the effectiveness of the flocking control algorithm.

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Flocks formation model for self-interested UAVs

Cited by 5 publications

References 64 publications

Machine Learning Methods for UAV Flocks Management-A Survey

Machine Learning Methods for UAV Flocks Management-A Survey

Machine Learning Methods for Management UAV Flocks -- a Survey

A flocking control algorithm of multi-agent systems based on cohesion of the potential function

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