# Navigation and Positioning Analysis of Electric Inspection Robot Based on Improved SVM Algorithm

Authors: Yongfu Li; Yingkai Long; Mingming Du; Xiping Jiang; Xianfu Liu
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to improve the accuracy of electric inspection robot navigation and positioning, an improved SVM algorithm was proposed to improve the accuracy of inspection. The research focuses on sensor calibration technology, lane line detection and robot positioning technology, obstacle detection and tracking technology, and substation road scene understanding technology. The results show that the radar measurement results have great fluctuation and deviation due to the existence of noise, but the results are smoother after EKF estimation. Secondly, the accuracy of the improved SVM classifier in this paper is much higher than that of the traditional method, and the improvement effect is obvious.


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## Body

## 1. Introduction

Operation and maintenance is the key measure to ensure the safe and reliable operation of the power system. As a power system, the substation undertakes the important responsibility of transmitting power, transforming voltage and distributing power, and power distribution in the power system. The substation is geographically distributed in a wide area, with a large number and a wide variety; the operation state changes rapidly and needs to withstand the test of various climatic conditions of the operation environment. Its operation and maintenance account for a large proportion in the technology and management of the power grid company [1, 2]. Therefore, adopting new technologies and methods to improve the operation and maintenance level and efficiency of substation has always been the focus of power grid companies. For a long time, substation patrol inspection has been mainly completed manually. With the rapid development of power system, power grid companies are facing great pressure on patrol inspection [3, 4]. Therefore, the introduction of new technologies and methods to improve the operation and maintenance level and efficiency of substations has always been in the focus of energy network companies. For a long time now, substation inspections have been carried out mainly by hand. At a time when the energy system is developing rapidly, power grid companies are facing a huge workload [5]. In recent ten years, China has carried out the research and application of inspection robot and achieved gratifying results [6]. However, from a practical point of view, the current control robot system is still difficult to perform on its own [7]. Because the robot does not have the ability to understand the sensor information, even if it obtains the road environment information, it is unable to judge the road environment. Usually, it can only drive blindly according to the map and guide wire but can not adjust according to the real-time road conditions. Manual intervention is often required in the event of a problem, and the ability to work independently in an emergency is insufficient [8, 9]. In conclusion, the study of more intelligent energy control robots is based on the need to develop power systems on the one hand and the development of new technologies on the other. It can be said that the rapid development of energy control robot technology is beginning in the spring. Figure 1 shows a study of the positioning method of an electrically controlled robotic system based on an improved PSO + SVM algorithm.Figure 1
A positioning method of power inspection robot system based on improved PSO + SVM algorithm.


## 2. Literature Review

The research on the operation and maintenance robot of power system started early. About 30 years ago, an intelligent robot for substation inspection and transmission line inspection was developed [10]. In 1984, Japan’s Tokyo Electric Power Company and Mitsubishi group began to jointly study the intelligent robot. The company implanted the visual servo technology into the robot to enable it to carry out automatic positioning and automatic recognition of three-dimensional objects. The robot walks along the ground track and automatically obtains the data information in the station. So far, it has been developed to the third generation [11]. In 2003, Japanese experts put forward the idea of substation inspection robot and began to carry out simulation test [12]. In 2005, the first substation inspection robot was successfully developed by American experts. Its main task of inspection is to carry out infrared temperature measurement, and it has been put into production and use by power companies in the western United States [13]. In 2008, American scientists proposed a navigation system composed of inertial navigation and global positioning system, which can make the robot have continuous, real-time, and efficient navigation ability [14]. Muthugala et al. proposed a robot navigation method based on digital map and machine vision, and its effectiveness was proved by experiments. After that, experts and scholars from various countries began to study robot navigation methods, and many robot navigation methods have been proposed successively, such as RIFD positioning method, computer vision-based navigation method, and depth learning-based navigation method. The proposal of these methods has gradually improved the autonomous navigation ability of substation inspection robot, which is of great significance to the practicability and popularization of substation inspection robot [15]. The research on inspection robot in China started relatively late compared with foreign countries, but many achievements have been made in recent years [16, 17]. Around 2004, with the support of the 863 project during the Tenth Five-Year Plan period, many units, including Shandong University, Tsinghua University, and Shandong electric power academy, carried out in-depth research on transmission line inspection robots and substation inspection robots [17, 18]. In the field of transmission line inspection robot, Professor Wu Gongping’s team of Wuhan University has made great research achievements and important social impact. The inspection robot along the ground wire developed by the team can transform the shockproof hammer and suspension clamp on the ground wire into an unobstructed road structure, so as to realize the efficient and safe inspection of the inspection robot along the whole line [19]. The autonomous navigation technology of substation inspection robot can be divided into the following parts: sensor calibration technology, lane line detection technology, obstacle detection technology, road scene description, and understanding technology. The relationship between them and the system block diagram is shown in Figure 2. This paper will focus on these parts.Figure 2
Block diagram of autonomous navigation system.


## 3. Research Methods

### 3.1. Lane Line Detection Based on Line Detection and Color Space Transformation

For the autonomous navigation of substation intelligent inspection robot, the most basic thing is to know its own local positioning [20]. Only by understanding the lane information can we obtain the position and direction of the robot relative to the lane. In the substation environment, most road scenes are structured roads. The so-called structured roads refer to standardized roads with clear lane signs, road boundaries, etc. [21]. The problem of how to extract accurate information from the outdoor environment, such as the amount of visual sensors, is often accompanied by a large amount of visual interference, which can not obtain accurate information from the outdoor environment.The road detection method studied in this paper is applied in the substation environment. At present, most of the substation roads are structured roads, the lane lines are yellow and clear, and most of the roads are straight roads [22]. Even if there are a few curves, for the moving robot, the curvature radius of the lane line not far from the camera is very small, and the lane line can be approximated as a straight line [23]. Therefore, this paper will use the combination of Hough line detection and color space transformation to detect the lane line in the substation road. The processing flow is shown in Figure 3. Firstly, each frame image of the camera is obtained and converted into RGB format. On the one hand, the image is preprocessed by binarization and noise reduction; then, Canny edge detection is carried out, and then, Hough transform is used for line detection. Through the screening of line segments, the candidate road edge collection is obtained. On the other hand, the RGB image is transformed into HSV color space image. By limiting the threshold of three HSV channels, the yellow lane line area is extracted, and the lane line area is appropriately expanded by morphological operation. Finally, the detection results of the two parts are fused to obtain a straight line that can divide the road area and nonroad area, and the lane line extraction test is carried out on the substation road pictures under different lighting conditions and different pavement environments with MATLAB.Figure 3
Flow chart of lane line detection based on Hough transform and HSV color space.


### 3.2. Obstacle Tracking Based on Extended Kalman Filter

Extended Kalman filtering is the usage form of Kalman filtering in nonlinear systems, and the main idea is to find the best balance between the system state estimates at the next moment and the measurements obtained for state estimation. The fusion of the two is to continuously adjust a changing weight and finally obtain an infinitely close to the accurate value of the system at this moment. When the system is a linear model, the Kalman filter can give the optimal estimation, but in practical application, because the motion trajectory of the obstacle relative to the radar is nonlinear, and the radar measurement system itself is also nonlinear, it is necessary to approximate linearize the nonlinear system first and then use the Kalman filter for the optimal estimation [24]. This method is the extended Kalman filter method (EKF). The process of optimal estimation using extended Kalman filter is as follows:Firstly, the discrete process model is established, as shown in:(1)X^k−=fxk−1+W,where f is the nonlinear equation of the system and W is the process noise of the system. The update corresponds to the covariance matrix W of the system. The update formula is as follows:
(2)Pk−=Fk−1Pk−1Fk−1T+Q,where F is the Jacobian matrix form of the system and Q is the measurement noise of the observer. Then, calculate the Kalman gain, and the calculation formula is
(3)Kk=Pk−HkTHkPk−HkT+R−1,where H is the measurement matrix of the system and R is the covariance matrix of the measurement noise. Then, the optimal estimation value of the current state is calculated through Kalman gain, and the calculation formula is
(4)x^k=x^k−+KkZk−hx^k−,whereZk is the measured value of the system at time k and h is the measured Jacobian matrix. The covariance matrix P in the current update state is
(5)Pk=Pk−KkHPk−.


### 3.3. Front Road Scene Recognition Based on SVM

After getting the schematic diagram of the road scene ahead, it needs to be transformed into road environment information that can be understood by the robot. Firstly, the understanding method of the road scene ahead needs to be selected according to the application environment. The substation scene studied in this paper has the following characteristics:(1)
The working environment of intelligent robot is fixed, which is only the substation environment, and there is no need to distinguish different scenes or perceive the similarity of scenes(2)
The robot does not need to recognize the object in front. The obstacle in front is pedestrian or vehicle. For the robot, it can be attributed to the nonpassable area, and there is no need to know what the obstacle is. Therefore, the recognition of the road scene in front of the intelligent robot in the substation can be simplified to the state classification of the obstacles in front(3)
Because the application scenario of intelligent robot in substation is substation environment, it is difficult to obtain samples, it is not easy to collect a large number of images for training templates, and in practical application, the conditions for increasing a large number of training samples are also difficult to meet. Therefore, the classification problem of road environment in front of substation environment belongs to the classification problem of small samplesSupport vector machine is a classification algorithm in machine learning. It can obtain better statistical laws when the statistical samples are small. Around 2010, with the rapid improvement of computing power and the emergence of big data, neural network research rose rapidly and ushered in a climax of development. However, compared with neural network, SVM still has some advantages, such as the feature dimension is more than the number of samples. In this small sample learning problem, the use effect of neural network is poor, but support vector machine can still live up to expectations. The essence of classifying the input front scene schematic diagram is to classify the image features of these images. Therefore, we should first select the appropriate image features as the basis for classification. For the input front scene diagram, its main features are composed of two aspects: edge features and gray features. Therefore, the two features selected in this paper are directional gradient histogram (HOG) and gray level cooccurrence matrix (GLCM). The combination of the two forms the feature matrix of the complete front scene diagram. The definition of the HOG function consists of calculating the histogram of the radius in the region of the picture to form an image. The next step is to split the HOG function: make the image normal. The color of the image, then, makes the image’s color space the gamma satellite, so that it can reduce the volume of light and noise. Calculate the gradient of the image. Rotate the image by a [-1,0,1] gradient operator and its shift, and at the edge of each pixel, the gradient is full. In the vertical direction, you can figure out the gradient integrity and then use the following formula; the vertical and the horizontal gradient of the current pixel is as follows:(6)Gxx,y=Hx+1,y−Hx−1,y,Gyx,y=Hx,y+1−Hx,y−1,Gxx,y=Gxx,y2+Gyx,y2,αx,y=tan−1Gyx,yGxx,y.Of these,Gxx,y represents the horizontal gradient of the pixel, Gxx,y represents the vertical gradient, and Hx,y represents the value of the point pixel. Gx,y represents the size of the pixel point gradient, and αx,y represents the direction of the point gradient.


### 3.4. SVM Classification Experiment

The experimental process of classification is carried out on the schematic diagram of the road scene in front of the robot. The feature value extraction of the image, the training of the classifier, and the test of the training results are completed by MATLAB 2016b. The flow chart of the experiment is shown in Figure4.Figure 4
SVM experiment flow.


## 3.1. Lane Line Detection Based on Line Detection and Color Space Transformation

For the autonomous navigation of substation intelligent inspection robot, the most basic thing is to know its own local positioning [20]. Only by understanding the lane information can we obtain the position and direction of the robot relative to the lane. In the substation environment, most road scenes are structured roads. The so-called structured roads refer to standardized roads with clear lane signs, road boundaries, etc. [21]. The problem of how to extract accurate information from the outdoor environment, such as the amount of visual sensors, is often accompanied by a large amount of visual interference, which can not obtain accurate information from the outdoor environment.The road detection method studied in this paper is applied in the substation environment. At present, most of the substation roads are structured roads, the lane lines are yellow and clear, and most of the roads are straight roads [22]. Even if there are a few curves, for the moving robot, the curvature radius of the lane line not far from the camera is very small, and the lane line can be approximated as a straight line [23]. Therefore, this paper will use the combination of Hough line detection and color space transformation to detect the lane line in the substation road. The processing flow is shown in Figure 3. Firstly, each frame image of the camera is obtained and converted into RGB format. On the one hand, the image is preprocessed by binarization and noise reduction; then, Canny edge detection is carried out, and then, Hough transform is used for line detection. Through the screening of line segments, the candidate road edge collection is obtained. On the other hand, the RGB image is transformed into HSV color space image. By limiting the threshold of three HSV channels, the yellow lane line area is extracted, and the lane line area is appropriately expanded by morphological operation. Finally, the detection results of the two parts are fused to obtain a straight line that can divide the road area and nonroad area, and the lane line extraction test is carried out on the substation road pictures under different lighting conditions and different pavement environments with MATLAB.Figure 3
Flow chart of lane line detection based on Hough transform and HSV color space.


## 3.2. Obstacle Tracking Based on Extended Kalman Filter

Extended Kalman filtering is the usage form of Kalman filtering in nonlinear systems, and the main idea is to find the best balance between the system state estimates at the next moment and the measurements obtained for state estimation. The fusion of the two is to continuously adjust a changing weight and finally obtain an infinitely close to the accurate value of the system at this moment. When the system is a linear model, the Kalman filter can give the optimal estimation, but in practical application, because the motion trajectory of the obstacle relative to the radar is nonlinear, and the radar measurement system itself is also nonlinear, it is necessary to approximate linearize the nonlinear system first and then use the Kalman filter for the optimal estimation [24]. This method is the extended Kalman filter method (EKF). The process of optimal estimation using extended Kalman filter is as follows:Firstly, the discrete process model is established, as shown in:(1)X^k−=fxk−1+W,where f is the nonlinear equation of the system and W is the process noise of the system. The update corresponds to the covariance matrix W of the system. The update formula is as follows:
(2)Pk−=Fk−1Pk−1Fk−1T+Q,where F is the Jacobian matrix form of the system and Q is the measurement noise of the observer. Then, calculate the Kalman gain, and the calculation formula is
(3)Kk=Pk−HkTHkPk−HkT+R−1,where H is the measurement matrix of the system and R is the covariance matrix of the measurement noise. Then, the optimal estimation value of the current state is calculated through Kalman gain, and the calculation formula is
(4)x^k=x^k−+KkZk−hx^k−,whereZk is the measured value of the system at time k and h is the measured Jacobian matrix. The covariance matrix P in the current update state is
(5)Pk=Pk−KkHPk−.


## 3.3. Front Road Scene Recognition Based on SVM

After getting the schematic diagram of the road scene ahead, it needs to be transformed into road environment information that can be understood by the robot. Firstly, the understanding method of the road scene ahead needs to be selected according to the application environment. The substation scene studied in this paper has the following characteristics:(1)
The working environment of intelligent robot is fixed, which is only the substation environment, and there is no need to distinguish different scenes or perceive the similarity of scenes(2)
The robot does not need to recognize the object in front. The obstacle in front is pedestrian or vehicle. For the robot, it can be attributed to the nonpassable area, and there is no need to know what the obstacle is. Therefore, the recognition of the road scene in front of the intelligent robot in the substation can be simplified to the state classification of the obstacles in front(3)
Because the application scenario of intelligent robot in substation is substation environment, it is difficult to obtain samples, it is not easy to collect a large number of images for training templates, and in practical application, the conditions for increasing a large number of training samples are also difficult to meet. Therefore, the classification problem of road environment in front of substation environment belongs to the classification problem of small samplesSupport vector machine is a classification algorithm in machine learning. It can obtain better statistical laws when the statistical samples are small. Around 2010, with the rapid improvement of computing power and the emergence of big data, neural network research rose rapidly and ushered in a climax of development. However, compared with neural network, SVM still has some advantages, such as the feature dimension is more than the number of samples. In this small sample learning problem, the use effect of neural network is poor, but support vector machine can still live up to expectations. The essence of classifying the input front scene schematic diagram is to classify the image features of these images. Therefore, we should first select the appropriate image features as the basis for classification. For the input front scene diagram, its main features are composed of two aspects: edge features and gray features. Therefore, the two features selected in this paper are directional gradient histogram (HOG) and gray level cooccurrence matrix (GLCM). The combination of the two forms the feature matrix of the complete front scene diagram. The definition of the HOG function consists of calculating the histogram of the radius in the region of the picture to form an image. The next step is to split the HOG function: make the image normal. The color of the image, then, makes the image’s color space the gamma satellite, so that it can reduce the volume of light and noise. Calculate the gradient of the image. Rotate the image by a [-1,0,1] gradient operator and its shift, and at the edge of each pixel, the gradient is full. In the vertical direction, you can figure out the gradient integrity and then use the following formula; the vertical and the horizontal gradient of the current pixel is as follows:(6)Gxx,y=Hx+1,y−Hx−1,y,Gyx,y=Hx,y+1−Hx,y−1,Gxx,y=Gxx,y2+Gyx,y2,αx,y=tan−1Gyx,yGxx,y.Of these,Gxx,y represents the horizontal gradient of the pixel, Gxx,y represents the vertical gradient, and Hx,y represents the value of the point pixel. Gx,y represents the size of the pixel point gradient, and αx,y represents the direction of the point gradient.


## 3.4. SVM Classification Experiment

The experimental process of classification is carried out on the schematic diagram of the road scene in front of the robot. The feature value extraction of the image, the training of the classifier, and the test of the training results are completed by MATLAB 2016b. The flow chart of the experiment is shown in Figure4.Figure 4
SVM experiment flow.


## 4. Result Analysis

### 4.1. Obstacle Tracking Experiment

MATLAB is used to simulate an obstacle with nonlinear motion relative to the robot. The effectiveness of extended Kalman filter in obstacle tracking is verified by comparing the errors between the measured value, estimated value, and real value before and after adding extended Kalman filter. Figure5 shows the relationship between the actual value, observed value, and EKF estimated value of obstacle motion. In Figure 5, “∗” represents the result of radar measurement, “+” represents the result after EKF estimation, and “-” represents the actual motion trajectory. It can be seen from the figure that due to the existence of noise, there are large fluctuations and deviations in the radar measurement result, and the result is smoother after EKF estimation.Figure 5
Relationship between actual value, observed value, and EKF estimated value of obstacle movement.


### 4.2. SVM Classifier Test

The feature vector extracted from the training set and the corresponding sample label are used as the input of SVM, and LIBSVM is used to train the semantic classification of images. Because different kernel functions will lead to different classification results of support vector machine for nonlinear SVM classification problem, the selection of kernel function has an important impact on the performance of support vector machine. Considering that Gaussian radial basis function (RBF kernel) has fewer parameters and is easy to calculate and RBF kernel is a kernel function with strong locality, high flexibility, and accuracy, and it is also the most widely used kernel function. This paper uses Gaussian radial basis function (RBF kernel) to train radial basis function SVM classifier. The specific test results are shown in Figure6.Figure 6
Comparison of classifier accuracy.As shown in Figure6, the accuracy of the improved SVM classifier in this article is much higher than that of the traditional method, and the effect of the improvement is obvious.


## 4.1. Obstacle Tracking Experiment

MATLAB is used to simulate an obstacle with nonlinear motion relative to the robot. The effectiveness of extended Kalman filter in obstacle tracking is verified by comparing the errors between the measured value, estimated value, and real value before and after adding extended Kalman filter. Figure5 shows the relationship between the actual value, observed value, and EKF estimated value of obstacle motion. In Figure 5, “∗” represents the result of radar measurement, “+” represents the result after EKF estimation, and “-” represents the actual motion trajectory. It can be seen from the figure that due to the existence of noise, there are large fluctuations and deviations in the radar measurement result, and the result is smoother after EKF estimation.Figure 5
Relationship between actual value, observed value, and EKF estimated value of obstacle movement.


## 4.2. SVM Classifier Test

The feature vector extracted from the training set and the corresponding sample label are used as the input of SVM, and LIBSVM is used to train the semantic classification of images. Because different kernel functions will lead to different classification results of support vector machine for nonlinear SVM classification problem, the selection of kernel function has an important impact on the performance of support vector machine. Considering that Gaussian radial basis function (RBF kernel) has fewer parameters and is easy to calculate and RBF kernel is a kernel function with strong locality, high flexibility, and accuracy, and it is also the most widely used kernel function. This paper uses Gaussian radial basis function (RBF kernel) to train radial basis function SVM classifier. The specific test results are shown in Figure6.Figure 6
Comparison of classifier accuracy.As shown in Figure6, the accuracy of the improved SVM classifier in this article is much higher than that of the traditional method, and the effect of the improvement is obvious.


## 5. Conclusion

In order to realize substation intelligent inspection, it is imperative to develop substation inspection robot with autonomous navigation ability, and making the robot have the ability of environmental perception and intelligent information processing is the basis of autonomous navigation. Therefore, the research work of this paper mainly focuses on sensor calibration technology, lane line detection and robot positioning technology, obstacle detection and tracking technology, and substation road scene understanding technology. The detection of lane lines and obstacles is the basis of road scene understanding, and the calibration of sensors is a bridge to integrate the detection results of lane lines and obstacles. The innovation of this paper is as follows: combined with the environmental characteristics of substation, this paper explores and improves the methods of robot environment perception and scene understanding. The main research work of this paper includes the following:(1)
A schematic diagram of the road ahead scene based on multisensor data fusion is proposedFor the macro description of the road scene in front of the robot, a geometric schematic diagram of the black-and-white road scene in front of the robot is proposed. Firstly, the results of lane line detection are used to distinguish road and nonroad areas. Through the processing of radar data, the characteristic information that can characterize the orientation and size of obstacles is extracted, and the results of sensor fusion are projected onto the image. The images of marked lane lines and obstacles are transformed into aerial view by inverse perspective transformation. Black represents the nonpassable area, and white represents the passable area. In black-and-white geometric form, the schematic diagram succinctly and intuitively shows whether a certain area is passable without redundant information, so as to highlight the characteristics of road obstacles and reduce the difficulty of robot scene understanding. Moreover, the schematic diagram is simple to obtain and can adapt to the changes of natural conditions such as lighting.(2)
The front road scene recognition based on SVM is realizedTo understand the front road map, an auxiliary vector machine (SVM) method is used to classify the front road map and define a schematic diagram. First, test the training package and the front road map diagram; then, write a function to extract and merge the hog feature and GLCM feature of the road scene diagram in front; then, the function is written and trained by using the extracted feature values of the front road scene, so as to obtain a multiclassification nonlinear SVM classifier, which can predict and classify the location of obstacles in the front road scene; finally, write a function to test the trained SVM classifier and visualize the test results. The results of the experiment show that the method can filter the position of obstacles in the front path with high accuracy, and the image recognition function is important to guide the further operation of the robot.



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*Source: 4613931-2022-07-25.xml*