Agreement algorithm using the trial and error method at the macrolevel

Phung, Nhuhai; Kubo, Michinori; Satō, Hiroshi; Iwanaga, Saori

doi:10.1007/s10015-018-0489-z

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…Fig. 4 illustrates some examples of the distributions of bias determined by (11). As can be seen, the shape of the bias distribution is a peak determined by the number of options only.…”

Section: B Distribution Of Biasmentioning

confidence: 98%

“…3). [11] We have extended Iwanaga and Namatame model to the best-of-n case with . To avoid confusion, the number of options is written as instead of in following.…”

Section: ( ) : ( )mentioning

confidence: 99%

“…To this problem, we have proposed Bias and Raising Threshold (BRT) algorithm that enables robots to deal with a large number of options in a trial and error manner by introducing a threshold term, that its value increases by the time elapsed, into individual' decision-making process [11]. Each robot changes its option by monitoring the number of supporters of its recurrent option.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Searching by Bias and Raising Threshold Algorithm Using Multiple Voting in the Best-of-n Problem

Phung¹,

Kubo²,

Satō³

2019

IJCTE

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The Bias and Raising Threshold (BRT) algorithm is one of the methods for the best-of-n problem (BSTn) that allows a group of robots to find out the appropriate collective option among a set of n alternatives. This paper improves the BRT algorithm by using multiple voting for shortening the search time. Concretely, each robot is considered that might be able to vote multiple times in a selection. The experimental results revealed that the search time was only dramatically reduced but also the search accuracy was improved, especially in difficult problems where there are a large number of options ().

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“…Fig. 4 illustrates some examples of the distributions of bias determined by (11). As can be seen, the shape of the bias distribution is a peak determined by the number of options only.…”

Section: B Distribution Of Biasmentioning

confidence: 98%

“…3). [11] We have extended Iwanaga and Namatame model to the best-of-n case with . To avoid confusion, the number of options is written as instead of in following.…”

Section: ( ) : ( )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Searching by Bias and Raising Threshold Algorithm Using Multiple Voting in the Best-of-n Problem

Phung¹,

Kubo²,

Satō³

2019

IJCTE

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show abstract

“…However, these methods does not consider to change formation shape of swarm robots adaptively in the environment with obstacles. In addition, there are many methods such as: behavior-based control methods [15,16], virtual structure methods [17,18], leader-follower control methods [19,20], and biological heuristic methods [21,22]. However, these methods have not solved these problems comprehensively.…”

Section: Introductionmentioning

confidence: 99%

AGENT: An Adaptive Grouping Entrapping Method of Flocking Systems

Wang¹,

Gu²,

Kuang³

et al. 2022

Preprint

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This study proposes a distributed algorithm that makes agents' adaptive grouping entrap multiple targets via automatic decision making, smooth flocking, and well-distributed entrapping. Agents make their own decisions about which targets to surround based on environmental information. An improved artificial potential field method is proposed to enable agents to smoothly and naturally change the formation to adapt to the environment. The proposed strategies guarantee that the coordination of swarm agents develops the phenomenon of multiple targets entrapping at the swarm level. We validate the performance of the proposed method using simulation experiments and design indicators for the analysis of these simulation and physical experiments.

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Automated pattern generation for swarm robots using constrained multi-objective genetic programming

Fan,

Wang,

et al. 2023

Swarm and Evolutionary Computation

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Agreement algorithm using the trial and error method at the macrolevel

Cited by 8 publications

References 5 publications

Efficient Searching by Bias and Raising Threshold Algorithm Using Multiple Voting in the Best-of-n Problem

Efficient Searching by Bias and Raising Threshold Algorithm Using Multiple Voting in the Best-of-n Problem

AGENT: An Adaptive Grouping Entrapping Method of Flocking Systems

Automated pattern generation for swarm robots using constrained multi-objective genetic programming

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