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A global path planning algorithm based on the feature map

IET Cyber-Systems and Robotics 2022年第1期4卷 15-24页

作者： Gongchang Ren Peng Liu Zhou He College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi'anChina School of Electrical and Control Engineering Shaanxi University of Science and TechnologyXi'anChina

The feature map is a characteristic of high computational efficiency,but it is seldom used in path planning due to its lack of expression of environmental details.To solve this problem,a global path planning algorithm based on the feature map is proposed based on the directionality of line segment features.First,the robot searches the path along the direction of the target position but turns to search in the direction parallel to the obstacle,which it approaches until the line between the robot and the target position does not intersect with obstacles.Then it turns to the target position,keep searching the path.Meanwhile,the problems of the direction selection of turning point,corner point and obstacle circumvention in the searching process are analysed and corresponding solutions are put forth.Finally,a path optimisation algorithm with variable parameters is proposed,making the optimised path shorter and smoother.Simulation experiments demonstrate that the proposed algorithm is superior to A*algorithm in terms of computation time and path length,especially of the computation efficiency.

关键词： A*algorithm feature map global path planning path optimisation variable parameters

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Optimal CNN-based semantic segmentation model of cutting slope images

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Frontiers of Structural and Civil Engineering 2022年第4期16卷 414-433页

作者： Mansheng LIN Shuai TENG Gongfa CHEN Jianbing LV Zhongyu HAO School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China JSTI Group Guangdong Inspection and Certification Co.Ltd Nanjing 210000China

This paper utilizes three popular semantic segmentation networks,specifically DeepLab v3+,fully convolutional network(FCN),and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection.The elements of cutting slope images are divided into 7 categories.In order to determine the best algorithm for pixel level classification of cutting slope images,the networks are compared from three aspects:a)different neural networks,b)different feature extractors,and c)2 different optimization algorithms.It is found that DeepLab v3+with Resnet18 and Sgdm performs best,FCN 32s with Sgdm takes the second,and U-Net with Adam ranks third.This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization.Results show that the contour generated by DeepLab v3+(combined with Resnet18 and Sgdm)is closest to the ground truth,while the resulting contour of U-Net(combined with Adam)is closest to the input images.

关键词： slope damage image recognition semantic segmentation feature map visualizations

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A Study on Classification and Detection of Small Moths Using CNN Model

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Computers, Materials & Continua 2022年第4期71卷 1987-1998页

作者： Sang-Hyun Lee Department of Computer Engineering Honam UniversityGwangsanguGwangju62399South Korea

Currently,there are many limitations to classify images of small objects.In addition,there are limitations such as error detection due to external factors,and there is also a disadvantage that it is difficult to accurately distinguish between various objects.This paper uses a convolutional neural network(CNN)algorithm to recognize and classify object images of very small moths and obtain precise data images.A convolution neural network algorithm is used for image data classification,and the classified image is transformed into image data to learn the topological structure of the image.To improve the accuracy of the image classification and reduce the loss rate,a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor.As a result of this study,we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths.Experimental results showed that the accuracy of classification of very small moths was more than 90%.

关键词： Convolution neural network rectified linear unit activation function pooling feature map

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