A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps

Mendona, Mrcio; Chrun, Ivan Rossato; Neves, Flvio; Arruda, Lúcia Valéria Ramos de

doi:10.1016/j.engappai.2016.12.017

Cited by 32 publications

(12 citation statements)

References 34 publications

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“…Several research groups look into navigation tasks and self-orientation, such as simultaneous localization and mapping (SLAM) [ 6 , 7 , 8 , 9 ]. Yet another important topic is the coordination of multi-AGV systems; in this field, the so-called integrated Markov neural reliability computation method [ 10 ], cloud solutions [ 11 ], colored Petri nets (CPNs) [ 12 ], conflict avoidance strategies [ 13 ], re-scheduling algorithms [ 14 ], dynamic fuzzy cognitive maps [ 15 ], as well as fuzzy logic and genetic algorithms [ 16 ], are applied. The enormous importance of fault-tolerant control (FTC) as well as fault detection and identification (FDI) is addressed by several research activities.…”

Section: State Of the Artmentioning

confidence: 99%

A Fuzzy Virtual Actuator for Automated Guided Vehicles

Stetter

2020

Sensors

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In the last decades, virtual sensors have found increasing attention in the research community. Virtual sensors employ mathematical models and different sources of information such as actuator states or sensors, which are already existing in a system, in order to generate virtual measurements. Additionally, in recent years, the concept of virtual actuators has been proposed by leading researchers. Virtual actuators are parts of a fault-tolerant control strategy and aim to accommodate faults and to achieve a safe operation of a faulty plant. This paper describes a novel concept for a fuzzy virtual actuator applied to an automated guided vehicle (AGV). The application of fuzzy logic rules allows integrating expert knowledge or experimental data into the decision making of the virtual actuator. The AGV under consideration disposes of an innovative steering concept, which leads to considerable advantages in terms of maneuverability, but requires an elaborate control system. The application of the virtual actuator allows the accommodation of several possible faults, such as a slippery surface under one of the drive modules of the AGV.

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Section: State Of the Artmentioning

confidence: 99%

A Fuzzy Virtual Actuator for Automated Guided Vehicles

Stetter

2020

Sensors

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“…These principles are important as a robot must be able of reacting to obstacles and emerging events quickly (Steels, 1995). Multiple cooperating robots can perform tasks more reliably and faster than a single robot, as the communication between a group of robots can multiply their effectiveness (Mendonça et al, 2017). Multi-Agent Systems (MAS) are usually required to solve problems in dynamic and unpredictable environments.…”

Section: Mobile Autonomous Robotsmentioning

confidence: 99%

“…The term "Collective Intelligence", in MAS, refers to elaborated collective behaviors that can arise from a combination of many agents. With relatively simple behavior and action, each operating autonomously (Mendonça et al, 2017).…”

Section: Mobile Autonomous Robotsmentioning

confidence: 99%

Fuzzy Cognitive Map Applied in Low-Cost Mobile Robot Exploring Unknown Environment

Mendonça

Ribeiro

Palácios

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Autonomous Navigation Systems still have some barriers to break. The decision-making process of autonomous robots tend to have high computational costs, and still feel distant to being used in everyday tasks. This paper presents an evolution of a previous work, where the major aspect discerning both papers is the objective finding routine, as the robot does not know its destination anymore. We propose using Fuzzy Cognitive Maps (FCM) to control the decision-making process of a low-cost autonomous mobile robot, that can be used for modeling and testing small scale prototypes. The system is embedded using an ATMega2560 microcontroller and the motors' actuation will be inferred directly from sensors reading. This approach presents a smoother detour and optimized trajectory, when compared to a multivalued logic controller. With low computational costs, and minimal adjustments, FCMs have proven to be useful as an embedded decision-making mechanism. Finally, future works will conclude this research. Resumo: Sistemas de Navegação Autônoma ainda possuem algumas barreiras a serem transpassadas. O processo de tomada de decisão de robôs autônomos tende a ter alto custo computacional, e ainda parece estar distante de ser usado em tarefas cotidianas. Este artigo apresenta uma evolução de um trabalho anterior. O principal aspecto que difere os dois trabalhos é a rotina de busca objetiva, pois o robô não conhece mais seu destino. A utilização de Mapas Cognitivos Fuzzy (FCM) é proposta para controlar o processo de tomada de decisão de um robô móvel autônomo de baixo custo, que pode ser usado para modelar e testar protótipos de pequena escala. O sistema é embarcado usando um microcontrolador ATMega2560 e a atuação dos motores será inferida diretamente da leitura dos sensores. Essa abordagem apresenta um desvio mais suave e uma trajetória otimizada, quando comparada a um controlador de lógica multivalorada. Com baixos custos computacionais e ajustes mínimos, os FCMs provaram ser úteis como um mecanismo incorporado de tomada de decisão. Finalmente, trabalhos futuros concluirão esta pesquisa.

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“…One is to extend the existing DFCM, make it can deal with the uncertain data. But till to now, the definition of DFCM is ambiguous, basically, the existing DFCM can be categorized into two groups, the DFCM base on the rules [15], [36], [37] and the DFCM base on the proportional regulation [38]- [40]. The mechanics of the dynamic for DFCMs should be clarified to realize the uncertainty extension of DFCM.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Extensions of Fuzzy Grey Cognitive Maps

et al. 2021

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Fuzzy Cognitive Map is recognized as an important model in soft computing. The Fuzzy Cognitive Map theory studies often treat uncertainty modeling and dynamic modeling separately. So far, there is no single Fuzzy Cognitive Map that can deal with uncertain data and dynamic environments simultaneously. This paper proposed an environment model to describe the link between changing weights and the dynamic environment. As the extensions of the classic Fuzzy Grey Cognitive Map, two dynamic models were designed and implemented in this work: Dynamic Fuzzy Grey Cognitive Map and Dynamic Fuzzy General Grey Map.In this work, we also analyze the characteristics of the two models and evaluate their performance using industrial control problems. The results showed that the proposed models could well handle uncertain data in dynamic environments.

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A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps

Cited by 32 publications

References 34 publications

A Fuzzy Virtual Actuator for Automated Guided Vehicles

A Fuzzy Virtual Actuator for Automated Guided Vehicles

Fuzzy Cognitive Map Applied in Low-Cost Mobile Robot Exploring Unknown Environment

The Dynamic Extensions of Fuzzy Grey Cognitive Maps

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