Control of Omnidirectional Robot Using Z-Number-Based Fuzzy System

Abiyev, Rahib H.; Akkaya, Nurullah; Günsel, Irfan

doi:10.1109/tsmc.2018.2834728

Cited by 64 publications

(35 citation statements)

References 40 publications

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“…(a) mechanical construction of the omni-vision camera: (1) cone mirror, (2) acrylic tube as protection support, (3) focus lens, (4) the camera, and (b) the 360° image. , , are the angle of the robot body with respect to its axis tube is used to strengthen the structure of the camera system that is resistant to impacts of balls or other objects.…”

Section: Omni-vision Cameramentioning

confidence: 99%

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CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

Dikairono¹,

Setiawardhana²,

Purwanto³

et al. 2020

IJIES

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The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

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Section: Omni-vision Cameramentioning

confidence: 99%

“…Research on ball position for MSL Goal Keeper robot has been discussed in [2]. Studies on motion controls of SSL soccer robot with four wheeled omnidirectional has been conducted in [3,4]. Modeling of three-wheels omnidirectional of MSL soccer robot has been discussed in [5].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

Dikairono¹,

Setiawardhana²,

Purwanto³

et al. 2020

IJIES

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show abstract

“…Aliev et al [28] proposed Z-number based Fuzzy system for the Multi-Criteria Decision-Making (MCDM), and Kang et al [29] proposed the method to convert Z-number to Fuzzy number. And, in [30], an omnidirectional robot was shown to navigate effectively by using Z-number based FL, in which the controller is able to reach a number of waypoints in the plane under carefully fixed steering angles.…”

Section: Introductionmentioning

confidence: 99%

“…• We present a Z-number based Fuzzy Logic (Z-FL) trajectory tracking control scheme with rules modeling the multi-input and multi-output nature of mobile robots. Our approach is inspired by the study of [30]; however, unlike [30], our scheme encodes the control rules only based on instantaneous measurements of distance to target and orientation gaps, which is more amenable to model control rules with finer and non-linear landscape, and more amenable to evaluate and to adapt the performance at high resolution, being relevant for practical navigation scenarios. Also, unlike [30], our approach avoids using the gradient of the tracking errors, which is due to our aim in evaluating the performance frontiers of our approach to navigate in scenarios with varying curvature.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

et al. 2020

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Being trajectory tracking key for safe mobile robot navigation, Fuzzy Logic (FL) has been useful in tackling uncertainty and imprecision to realize robust and smooth trajectory tracking. In this paper, we present the Z-number based Fuzzy Logic control for trajectory tracking of differential wheeled mobile robots. The unique point of our approach lies in the ability to encode constraint and reliability in multi-input and multi-output rules, whose antecedent universe considers only the instantaneous measurements of distance and the orientation gaps, and whose consequent universe is computed by the interpolative reasoning and the graded mean integration approach. As a consequence, not only our approach avoids the complexity of encoding error gradients, but also is advantageous to model versatile control rules able to cope with missing observations and noisy inducements on actuators. Our experiments using both physics-based simulations and real-world tests based on a Pioneer 3DX robot architecture have elucidated the superior efficacy and the feasibility of the proposed controller regarding accuracy, robustness, and smoothness compared to other well-known related frameworks such as Fuzzy Logic Type 1, Fuzzy Logic Type 2 and Fuzzy Logic with PID. Our results provide unique insights to realize generalizable algorithms aided by FL and Z-number towards robust trajectory tracking.

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“…In [14] numerical solution of linear regression based on Z-Numbers by the improved neural network is proposed. In [3] a Z-number-based fuzzy inference system for control of the omnidirectional soccer robot is suggested. A general and computationally effective method to calculation with discrete Z-numbers is proposed in [5].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approach to Solving Fuzzy Nonlinear Equations using Z-Numbers

Jafari

Razvarz

Gegov

2019

IEEE Trans. Fuzzy Syst.

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In this work, the fuzzy property is described by means of the Z-number as the coefficients and variables of the fuzzy equations. This alteration for the fuzzy equation is appropriate for system modeling with Z-number parameters. In this paper, the fuzzy equation with Z-number coefficients and variables is tended to be used as the models for the uncertain systems. The modeling issue related to the uncertain system is to obtain the Z-number coefficients and variables of the fuzzy equation. Nevertheless, it is extremely hard to get the Z-number coefficients of the fuzzy equations. In this paper in order to model the uncertain nonlinear systems, a novel structure of the multilayer neural network is utilized in such a manner that it is able to obtain the Z-number coefficients of the fuzzy equation. The suggested technique is validated by some examples with applications.

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Control of Omnidirectional Robot Using Z-Number-Based Fuzzy System

Cited by 64 publications

References 40 publications

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

Neural Network Approach to Solving Fuzzy Nonlinear Equations using Z-Numbers

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