|Place of Conferral||北京|
|Keyword||管廊机器人 巡检 消失点 分割 导航|
|Other Abstract||In modern society, the urban land is tense, the traffic pressure is great, and the demand for city appearance is high. The underground cable thus becomes the main transmission method of the power grid system in the urban or densely populated areas. Its reliable and stable operation is the basis for power grid security and high-quality power supply. At present, the inspection of underground cables is still dominated by manpower, underground environmental conditions are poor, labor intensity of inspections is large, and the danger is high. There is still no effective automated inspection method. With the rapid development of robotics technology and its in-depth application in different fields, it is an inevitable trend to apply mobile robots to the real-time monitoring of underground transmission lines. The robot enters the cable duct for inspection operations, which can greatly reduce the labor intensity of the workers, improve the work efficiency, avoid the danger of manual operations, and reduce the probability of cable faults and cable trench accidents. Therefore, the research on the inspection robot of power transmission pipelines has important application value and practical significance for improving the transmission quality and ensuring the transmission safety.|
This paper aims at improving the overall control performance of the trackless pipeline inspection mobile robot, and carries out the research work on the key technologies in inspection task of the pipeline robot. The mainly researches are environment perception algorithm and the robot navigation control algorithm. The main work of the dissertation is as follows:
1、A key point detection algorithm based on deep learning is proposed and applied to the problem of road vanishing point detection. Based on the AlexNet network, combined with the vanishing point detection problem, the network structure was modified, the activation function was replaced, an additional layer was added, the loss function was modified, and it was applied to the detection of vanishing points. The experimental results show that the method proposed in this paper is highly adaptable, and it still has a good effect on fuzzy pictures that are difficult to handle with traditional methods.
2、The FCN network was used to study the segmentation of the pavement in the pipeline. According to the characteristics of the underground gallery, the FCN's network structure was simplified, and a key layer representing the vanishing point location was added . The algorithm integrates the vanishing point information into the road segmentation, uses the vanishing point position to guide the road segmentation, and realizes the pixel level classification of the road surface. Experiments show that this method performs better for the underground gallery environment than the ordinary deep learning segmentation algorithm.
3、The conversion algorithm for inspection robot simulation environment and real environment was proposed and implemented. Based on variational auto-encoder, generative adversarial algorithm, and loop generative adversarial algorithm, a network of unsupervised image conversion was established, and an algorithm capable of converting the robot simulation environment and the robot's real environment was realized. This will enable visual navigation based on learning algorithms to adapt more quickly to real scenes.
4、The reinforcement learning method is used to achieve the path navigation of mobile robots in the corridor environment. By establishing a simulation environment, the input of the laser radar is taken as the status, and the output of the agent is used as the steering control of the robot. The algorithm realizes autonomous navigation of the robot under the simulation environment through deep Q-learning algorithm. Through continuous learning in the environment, robots find the best actions in different states. This will allow visual navigation based on learning algorithms to adapt to real-world situations faster.
|First Author Affilication||Institute of Automation, Chinese Academy of Sciences|
|闫帅. 地下管廊巡检机器人的环境感知与自主导航技术研究[D]. 北京. 中国科学院研究生院,2017.|
|Files in This Item:|
|闫帅-毕业论文.pdf（4375KB）||学位论文||暂不开放||CC BY-NC-SA||Application Full Text|
|Recommend this item|
|Export to Endnote|
|Similar articles in Google Scholar|
|Similar articles in Baidu academic|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.