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Article
Publication date: 21 August 2017

Autonomous location control of a robot manipulator for live maintenance of high-voltage transmission lines

Wei Jiang, Gongping Wu, Fei Fan, Wei Wang, Jie Zhang, Xuhui Ye and Peng Zhou

This paper aims to develop a robot for tightening charged bolt to solve the shortcomings of high labor intensity, low efficiency, high risk and poor reliability in…

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Abstract

Purpose

This paper aims to develop a robot for tightening charged bolt to solve the shortcomings of high labor intensity, low efficiency, high risk and poor reliability in artificially tightening drainage board bolt of strain clamp for high voltage transmission line. Realizing bolt-nut capture and location by manipulator is a critical process to complete the whole working task. To solve such key technology, an autonomous location control method for N-joint robot manipulator based on kinematics was proposed.

Design/methodology/approach

Through D-H kinematics analysis under flexible working environment of transmission line, the autonomous location control of double manipulators can be abstracted as a nonlinear approximation problem based on joint inverse kinematics. In addition, regarding the complex coupling relationship among different joint angles and the complex decoupling process which leads to the non-uniqueness of inverse solution, an improved backpropagation (BP) network was proposed based on the combination of dynamic adaptive adjustment of learning rate and variable momentum factor, so that the inverse kinematics of manipulator can be solved and the optimization evaluation mechanism of inverse solution can be presented. The proposed autonomous location control method is of adaptability to flexible environment and structural parameters of different drainage boards. The simulation results verified the effectiveness of the proposed method. Compared with the other location control, this method can achieve faster location speed, higher precision and lower hardware cost. Finally, the field operation test further validated that such autonomous location control method was of strong engineering practicability.

Findings

The proposed autonomous location control method is adaptable to a flexible environment and to the structural parameters of different types of drainage board. Simulation results confirm the effectiveness of the proposed method, which, in comparison with other approaches to location control, can achieve faster location, higher precision and lower hardware cost. Finally, a field test further confirms the engineering practicability of the proposed autonomous location control method.

Originality/value

The proposed method can achieve faster location speed, higher precision which meet the requirement of real-time control relative to the standard BP algorithm. Moreover, it is of strong adaptability to flexible environment and structural parameters for different drainage board. Field operation experiment further validated the engineering practicability of the method.

Details

Industrial Robot: An International Journal, vol. 44 no. 5
Type: Research Article
DOI: https://doi.org/10.1108/IR-08-2016-0220
ISSN: 0143-991X

Keywords

  • Robot
  • Kinematics
  • Autonomous location
  • Improved BP network

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Article
Publication date: 18 November 2019

Road roughness acquisition and classification using improved restricted Boltzmann machine deep learning algorithm

Qinghua Liu, Lu Sun, Alain Kornhauser, Jiahui Sun and Nick Sangwa

To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm…

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Abstract

Purpose

To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small.

Design/methodology/approach

The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness.

Findings

The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness.

Originality/value

This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

Details

Sensor Review, vol. 39 no. 6
Type: Research Article
DOI: https://doi.org/10.1108/SR-05-2018-0132
ISSN: 0260-2288

Keywords

  • Deep learning
  • Sensors
  • Neural networks
  • Road roughness
  • Adaboost Backward Propagation algorithm
  • Data acquisition system
  • Road engineering
  • Acquisition system
  • Deep neural network

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Article
Publication date: 12 October 2019

Research on configuration design and operation effect evaluation for ultra high voltage (UHV) vertical insulator cleaning robot

Yu Yan, Wei Jiang, An Zhang, Qiao Min Li, Hong Jun Li, Wei Chen and YunFei Lei

This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning…

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Abstract

Purpose

This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning in ultra high voltage (UHV) converter station, the purpose of this paper is to propose a basic configuration of UHV vertical insulator cleaning robot with multi-freedom-degree mechanical arm system on mobile airborne platform and its innovation cleaning operation motion planning.

Design/methodology/approach

The main factors affecting the insulators cleaning effect in the operation process have been analyzed. Because of the complex coupling relationship between the influencing factors and the insulators cleaning effect, it is difficult to establish its analytical mathematical model. Combining the non-linear mapping and approximation characteristics of back propagation (BP) neural network, the insulator cleaning effect evaluation can be abstracted as a non-linear approximation process from actual cleaning effect to ideal cleaning effect. An evaluation method of robot insulator cleaning effect based on BP neural network has been proposed.

Findings

Through the BP neural network training, the robot cleaning control parameters can be obtained and used in the robot online operation control, so that the better cleaning effect can be also obtained. Finally, a physical prototype of UHV vertical insulator cleaning robot has been developed, and the effectiveness and engineering practicability of the proposed robot configuration, cleaning effect evaluation method are all verified by simulation experiments and field operation experiments. At the same time, this method has the remarkable characteristics of sound versatility, strong adaptability, easy expansion and popularization.

Originality/value

An UHV vertical insulator cleaning robot operation system platform with multi-arm system on airborne platform has been proposed. Through the coordinated movement of the manipulator each joint, the manipulator can be positioned to the insulator strings, and the insulator can be cleaned by two pairs high-pressure nozzles located at the double manipulator. The influence factors of robot insulator cleaning effect have been analyzed. The BP neural network model of insulator cleaning effect evaluation has been established. The evaluation method of robot insulator cleaning effect based on BP neural network has also been proposed, and the corresponding evaluation result can be obtained through the network training. Through the system integration design, the robot physical prototype has been developed. For the evaluation of other operation effects of power system, the validity and engineering practicability of the robot mechanism, motion planning and the method for evaluating the effect of robot insulator cleaning have been verified by simulation and field operation experiments.

Details

Industrial Robot: the international journal of robotics research and application, vol. 47 no. 1
Type: Research Article
DOI: https://doi.org/10.1108/IR-08-2019-0167
ISSN: 0143-991X

Keywords

  • Robot
  • BP neural network
  • Configuration design
  • Effect evaluation
  • Insulator cleaning

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Article
Publication date: 12 August 2020

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

Jinsong Tu, Yuanzhen Liu, Ming Zhou and Ruixia Li

This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.

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Abstract

Purpose

This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.

Design/methodology/approach

The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.

Findings

Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.

Originality/value

The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
DOI: https://doi.org/10.1108/JEDT-01-2020-0022
ISSN: 1726-0531

Keywords

  • RATIC
  • BP neural network
  • Genetic algorithm
  • GA-BP neural network
  • Strength prediction

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Article
Publication date: 10 August 2010

Study on deformation prediction of landslide based on genetic algorithm and improved BP neural network

Yong‐fen Ran, Guang‐chi Xiong, Shi‐sheng Li and Liao‐yuan Ye

The purpose of this paper is to improve back propagation neural network (BPNN) modeling in order to promote the forecast calculation precision of landslide deformation.

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Abstract

Purpose

The purpose of this paper is to improve back propagation neural network (BPNN) modeling in order to promote the forecast calculation precision of landslide deformation.

Design/methodology/approach

The genetic algorithm is adopted to optimize the architectural parameter of BPNN so as to avoided errors occurrence while using the trial‐and‐error method. Furthermore, the Sigmoid function is improved and revised to expand the output range of change‐over function from unipolar (only positive) to ambipolar (may be positive or negative), then the convergence time is reduced and the neural network can express more artificial intelligence.

Findings

The modeling can effectively reduce the probability to get into the local minima while employing neural networks to forecast the landslide deformation. It significantly promotes the forecast precision.

Research limitations/implications

The improved BPNN modeling, which is very good in learning and processing information, can work out the complex non‐linear relation by learning model and using the present data or reciprocity of surroundings.

Practical implications

The revised BPNN modeling in this paper can be used to predict and calculate landslide deformation.

Originality/value

The paper demonstrates that the modeling can meet the demand of calculation precision.

Details

Kybernetes, vol. 39 no. 8
Type: Research Article
DOI: https://doi.org/10.1108/03684921011063529
ISSN: 0368-492X

Keywords

  • Cybernetics
  • Genetic algorithm
  • Back propagation neural network (BPNN)
  • Landslide
  • Deformation prediction

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Article
Publication date: 19 August 2019

Motion posture control for power cable maintenance robot in typical operation conditions

Wei Jiang, Meng Huai Peng, Yu Yan, Gongping Wu, An Zhang, Lianqing Yu and Hong Jun Li

In the extreme power environment of flexible transmission line, wind load, high voltage and strong electromagnetic interference, the motion performance of the robot…

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Abstract

Purpose

In the extreme power environment of flexible transmission line, wind load, high voltage and strong electromagnetic interference, the motion performance of the robot manipulator is strongly affected by the extreme environment. Therefore, this study aims to improve the manipulator motion control performance of power cable maintenance robot and effectively reduce the influence of specific operation environment on the robot manipulator motion posture.

Design/methodology/approach

The mathematical model under three typical operation conditions, namely, flexible line, wind load and strong electromagnetic field have been established, correspondingly the mapping relationship between different environment parameters and robot operation conditions are also given. Based on the nonlinear approximation feature of neural network, a back propagation (BP) neural network is adopted to solve the posture control problems. The power cable line sag, robot tile angle caused by wind load and spatial field strength are the input signals of the BP network in the robot motion posture control method.

Findings

Through the training and learning of the BP network, the output control variables are used to compensate the actual robot operation posture. The simulation experiment verifies the effectiveness of the proposed algorithm, and compared with the conventional proportional integral differential (PID) control, the method has high real-time performance and sound stability. Finally, field operation experiments further validate the engineering feasibility of the control method, and at the same time, the proposed control method has the remarkable characteristics of sound universality, adaptability and easy expansion.

Originality/value

A multi-layer control architecture which is suitable for smart grid platform maintenance is proposed and a robot system platform for network operation and maintenance management is constructed. The human–machine–environment coordination and integration mode and intelligent power system management platform can be realized which greatly improves the intelligence of power system management. Mathematical models of the robot under three typical operation conditions of flexible wire wind load and strong electromagnetic field are established and the mapping relationship between different environmental parameters and the robot operation conditions is given. Through the non-linear approximation characteristics of BP network, the control variables of the robot joints can be obtained and the influence of extreme environment on the robot posture can be compensated. The simulation results of MATLAB show that the control algorithm can effectively restrain the influence of uncertain factors such as flexible environment, wind load and strong electromagnetic field on the robot posture. It satisfied the design requirements of fast response, high tracking accuracy and good stability of the control system. Field operation tests further verify the engineering practicability of the algorithm.

Details

Industrial Robot: the international journal of robotics research and application, vol. 46 no. 5
Type: Research Article
DOI: https://doi.org/10.1108/IR-01-2019-0015
ISSN: 0143-991X

Keywords

  • Flexible wire
  • Posture control
  • Power cable maintenance robot
  • Flexible conductor
  • Wind load
  • Spatial electromagnetic field

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Article
Publication date: 19 June 2020

Research on the influence of background light on the accuracy of a three-dimensional coordinate measurement system based on dual-PSD

Xiaohong Lu, Yu Zhou, Jinhui Qiao, Yihan Luan and Yongquan Wang

The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under…

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Abstract

Purpose

The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light.

Design/methodology/approach

The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained.

Findings

The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy.

Originality/value

BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.

Details

Engineering Computations, vol. ahead-of-print no. ahead-of-print
Type: Research Article
DOI: https://doi.org/10.1108/EC-02-2020-0081
ISSN: 0264-4401

Keywords

  • PSD
  • Background light
  • MEA-BP neural network
  • Three-dimensional coordinate measurement

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Article
Publication date: 5 August 2019

sEMG-based shoulder-elbow composite motion pattern recognition and control methods for upper limb rehabilitation robot

Xiufeng Zhang, Jitao Dai, Xia Li, Huizi Li, Huiqun Fu, Guoxin Pan, Ning Zhang, Rong Yang and Jianguang Xu

This paper aims to develop a signal acquisition system of surface electromyography (sEMG) and use the characteristics of (sEMG) signal to interference action pattern.

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Abstract

Purpose

This paper aims to develop a signal acquisition system of surface electromyography (sEMG) and use the characteristics of (sEMG) signal to interference action pattern.

Design/methodology/approach

This paper proposes a fusion method based on combining the coefficient of AR model and wavelet coefficient. It improves the recognition rate of the target action. To overcome the slow convergence speed and local optimum in standard BP network, the study presents a BP algorithm which combine with LM algorithm and PSO algorithm, and it improves the convergence speed and the recognition rate of the target action.

Findings

Experiments verify the effectiveness of the system from two aspects the target motion recognition rate and the corresponding reaction speed of the robotic system.

Originality/value

The study developed a signal acquisition system of sEMG and used the characteristics of (sEMG) signal to interference action pattern. The myoelectricity integral values are presented to determine the starting point and end point of target movement, which is more effective than using single sample point amplitude method.

Details

Assembly Automation, vol. 39 no. 3
Type: Research Article
DOI: https://doi.org/10.1108/AA-11-2017-148
ISSN: 0144-5154

Keywords

  • BP network
  • Support vector machine (SVM)
  • Composite motion pattern recognition of shoulder-elbow
  • Upper limb rehabilitation training system

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Article
Publication date: 11 December 2020

A road traffic accidents prediction model for traffic service robot

Chaohui Zhang, Yijing Li and Tian Li

In recent years, the demand for road traffic has continued to increase, but the casualties and economic losses caused by traffic accidents have also remained high…

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Abstract

Purpose

In recent years, the demand for road traffic has continued to increase, but the casualties and economic losses caused by traffic accidents have also remained high. Therefore, the use of social service robots to manage, supervise and warn real-time traffic information has become an inevitable trend of traffic safety management.

Design/methodology/approach

In order to explore the inherent objective development law of road traffic accidents, in this paper, the factor analysis (FA) is used to explore the main influencing factors of traffic accidents, then the random forest algorithm is applied to build an FA–RF-based road traffic accident severity prediction model to predict two- and three-category accidents.

Findings

By comprehensively comparing the classification results of the two- and the three-category accident prediction, it also finds that due to the intersection between injuries and fatalities and the lack of necessarily external environmental information, the FA–RF model has a large degree of misjudgment for injuries and fatalities. Therefore, it is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.

Originality/value

(1) A fusion model of FA–RF is considered to predict traffic accidents, which can be applied in traffic service robot. (2) It is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
DOI: https://doi.org/10.1108/LHT-05-2020-0115
ISSN: 0737-8831

Keywords

  • Social robots
  • Traffic accidents
  • Factor analysis
  • Random forest
  • Influencing factors’
  • analysis
  • Severity forecasting

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Article
Publication date: 7 January 2019

Short-term load forecasting for microgrids based on DA-SVM

Anan Zhang, Pengxiang Zhang and Yating Feng

The study aims to accomplish the short-term load forecasting for microgrids. Short-term load forecasting is a vital component of economic dispatch in microgrids, and the…

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Abstract

Purpose

The study aims to accomplish the short-term load forecasting for microgrids. Short-term load forecasting is a vital component of economic dispatch in microgrids, and the forecasting error directly affects the economic efficiency of operation. To some extent, short-term load forecasting is more difficult in microgrids than in macrogrids.

Design/methodology/approach

This paper presents the method of Dragonfly Algorithm-based support vector machine (DA-SVM) to forecast the short-term load in microgrids. This method adopts the combination of penalty factor C and kernel parameters of SVM which needs to be optimized as the position of dragonfly to find the solution. It takes the forecast accuracy calculated by SVM as the current fitness value of dragonfly and the optimal position of dragonfly obtained through iteration is considered as the optimal combination of parameters C and s of SVM.

Findings

DA-SVM algorithm was used to do short-term load forecast in the microgrid of an offshore oilfield group in the Bohai Sea, China and the forecasting results were compared with those of PSO-SVM, GA-SVM and BP neural network models. The experimental results indicate that the DA-SVM algorithm has better global searching ability. In the case of study, the root mean square errors of DA-SVA are about 1.5 per cent and its computation time is saved about 50 per cent.

Originality/value

The DA-SVM model presented in this paper provides an efficient and effective method of short-term load forecasting for a microgrid electric power system.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 38 no. 1
Type: Research Article
DOI: https://doi.org/10.1108/COMPEL-05-2018-0221
ISSN: 0332-1649

Keywords

  • Adaptive control
  • Support vector machines

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