Using interval type-2 recurrent fuzzy cerebellar model articulation controller based on improved differential evolution for cooperative carrying control of mobile robots

Jyun Yu Jhang; Cheng Jian Lin; Tzu Chao Lin; Chao Chun Chen; Kuu-Young Young

doi:10.18494/SAM.2018.2052

Using interval type-2 recurrent fuzzy cerebellar model articulation controller based on improved differential evolution for cooperative carrying control of mobile robots

Jyun Yu Jhang, Cheng Jian Lin, Tzu Chao Lin, Chao Chun Chen, Kuu-Young Young

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

In this study, we propose an effective cooperative carrying method for mobile robots in an unknown environment. During the carrying process, the state manager (SM) switches between wall-following carrying (WFC) and toward-goal carrying (TGC) to avoid obstacles and prevent the objects from dropping. An interval type-2 recurrent fuzzy cerebellar model articulation controller (IT2RFCMAC) based on dynamic group differential evolution (DGDE) is proposed for implementing the WFC and TGC of mobile robots. The adaptive wall-following control is developed using the reinforcement learning strategy to realize cooperative carrying control for mobile robots. The experimental results indicated that the proposed DGDE is superior to other algorithms and can complete the cooperative carrying of mobile robots to reach the goal location.

Original language	English
Pages (from-to)	2499-2516
Number of pages	18
Journal	Sensors and Materials
Volume	30
Issue number	11
DOIs	https://doi.org/10.18494/SAM.2018.2052
State	Published - 2018

Keywords

Cooperative carrying
Differential evolution
Mobile robot
Reinforcement learning

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title = "Using interval type-2 recurrent fuzzy cerebellar model articulation controller based on improved differential evolution for cooperative carrying control of mobile robots",

abstract = "In this study, we propose an effective cooperative carrying method for mobile robots in an unknown environment. During the carrying process, the state manager (SM) switches between wall-following carrying (WFC) and toward-goal carrying (TGC) to avoid obstacles and prevent the objects from dropping. An interval type-2 recurrent fuzzy cerebellar model articulation controller (IT2RFCMAC) based on dynamic group differential evolution (DGDE) is proposed for implementing the WFC and TGC of mobile robots. The adaptive wall-following control is developed using the reinforcement learning strategy to realize cooperative carrying control for mobile robots. The experimental results indicated that the proposed DGDE is superior to other algorithms and can complete the cooperative carrying of mobile robots to reach the goal location.",

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AU - Jhang, Jyun Yu

AU - Lin, Cheng Jian

AU - Lin, Tzu Chao

AU - Chen, Chao Chun

AU - Young, Kuu-Young

N1 - Publisher Copyright: © MYU K.K.

PY - 2018

Y1 - 2018

N2 - In this study, we propose an effective cooperative carrying method for mobile robots in an unknown environment. During the carrying process, the state manager (SM) switches between wall-following carrying (WFC) and toward-goal carrying (TGC) to avoid obstacles and prevent the objects from dropping. An interval type-2 recurrent fuzzy cerebellar model articulation controller (IT2RFCMAC) based on dynamic group differential evolution (DGDE) is proposed for implementing the WFC and TGC of mobile robots. The adaptive wall-following control is developed using the reinforcement learning strategy to realize cooperative carrying control for mobile robots. The experimental results indicated that the proposed DGDE is superior to other algorithms and can complete the cooperative carrying of mobile robots to reach the goal location.

AB - In this study, we propose an effective cooperative carrying method for mobile robots in an unknown environment. During the carrying process, the state manager (SM) switches between wall-following carrying (WFC) and toward-goal carrying (TGC) to avoid obstacles and prevent the objects from dropping. An interval type-2 recurrent fuzzy cerebellar model articulation controller (IT2RFCMAC) based on dynamic group differential evolution (DGDE) is proposed for implementing the WFC and TGC of mobile robots. The adaptive wall-following control is developed using the reinforcement learning strategy to realize cooperative carrying control for mobile robots. The experimental results indicated that the proposed DGDE is superior to other algorithms and can complete the cooperative carrying of mobile robots to reach the goal location.

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KW - Reinforcement learning

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Using interval type-2 recurrent fuzzy cerebellar model articulation controller based on improved differential evolution for cooperative carrying control of mobile robots

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Keywords

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