A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

doi:10.1155/2015/606734

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…Several works report using such an approach; Skoundrianos et al used a local model neural network to diagnose faults in the wheels of a mobile robot [9]. Yuan et al [10] proposed a hybrid fault diagnosis approach based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and…”

Section: The State Of the Artmentioning

confidence: 99%

Monitoring a robot swarm using a data-driven fault detection approach

Khaldi¹,

Harrou²,

Cherif³

et al. 2017

Robotics and Autonomous Systems

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Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentiallyweighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T 2 and Q).

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Section: The State Of the Artmentioning

confidence: 99%

Monitoring a robot swarm using a data-driven fault detection approach

Khaldi¹,

Harrou²,

Cherif³

et al. 2017

Robotics and Autonomous Systems

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“…As shown in Fig. 5, the wheeled robot designed by our research group was applied as the experimental platform [17]. The robot was driven by two differential wheels and was equipped with various kinds of sensors, such as encoders, voltage detector, temperature sensor, etc.…”

Section: Implementation On Wheeled Robotmentioning

confidence: 99%

A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

et al. 2015

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Abstract-Support Vector Machines (SVM) are popular machine learning algorithms that have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which has been applied in the fault diagnosis of a service robot. First, sensor data were sampled under different running conditions of the robot and those samples were divided as training sets and testing sets. Second, the sampled data were preprocessed and a principal component analysis (PCA) model was established for fault feature extraction. Third, the feature vectors were used to train the SVM classifier, which achieved the fault diagnosis of the robot. To speed up the training process of the SVM, on the one hand, sample reduction was done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we took advantage of the excellent parallel computing abilities of a Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.Index Terms-SVM, fast training method, support vectors selection, GPU, robot fault diagnosis.

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“…Due to an extensive and wide range of solutions, the localization problem has always been widely investigated in autonomous robotics. [1][2][3][4] During the last few decades, many kinds of techniques and methods have been developed with varying levels of acceptance, such as dead reckoning based on an odometer, 5 algorithms based on wireless sensor networks, 6 inertial sensor-based algorithms, 7 and map-matching algorithms based on vision. 8 Among all available sensors, cameras have been increasingly applied to vision-based mobile robot localization due to their low prices, ease of use, and ability to offer abundant information.…”

Section: Introductionmentioning

confidence: 99%

An improved graph-based visual localization system for indoor mobile robot using newly designed markers

Zhu

et al. 2018

International Journal of Advanced Robotic Systems

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To address the localization problem for a mobile robot in an indoor environment, an improved visual localization system based on artificial markers is proposed in this article. First, we will present a novel artificial marker which can be detected and identified easily and correctly. We will then introduce how it is designed, recognized, and verified. The markers are arranged on the ceiling and the mobile robot moves around on the ground to capture images with an up-facing monocular camera. The markers' information, including the position and direction, will be obtained by processing the images. The camera's uncertainty model is then put forward based on the distortion of the camera. The uncertainty of the obtained markers' information is analyzed through the uncertainty model. Finally, with the obtained information, the marker map in global image coordinate system is established and optimized through a graph-based algorithm in which the edges can be updated to reduce the uncertainty using Bayes Estimation method. To verify the effectiveness of the localization system, numerous experiments have been conducted. Additionally, the proposed method has also been applied to industrial forklift to test its robustness in a factory environment.

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A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

Cited by 12 publications

References 32 publications

Monitoring a robot swarm using a data-driven fault detection approach

Monitoring a robot swarm using a data-driven fault detection approach

A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

An improved graph-based visual localization system for indoor mobile robot using newly designed markers

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