Thesis Advisor谭民 ; 赵晓光
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Name工学博士
Degree Discipline控制理论与控制工程
Keyword高压架空输电线路巡检 无人机 悬挂巡线小车 巡检环境SLAM 图像质量评估 电力设备目标检测 表观缺陷检测







Other Abstract

Intelligent robot high-voltage overhead transmission line inspection has important research values and broad application prospects. In this thesis, a new type of cooperative robot is developed for power line inspection and the detection method of surface defects along the power lines is studied. The main contributions of the thesis are as follows:

Firstly, the climbing inspection robots have limited obstacle avoidance capabilities, and difficulty in getting on and off the power line. The hybrid inspection robots have redundant mechanisms and unstable movements. To solve the weaknesses above, a new type of intelligent cooperative inspection robot system for high-voltage overhead transmission lines and a new inspection scheme are proposed and implemented. The system, which has complementary advantages, consists of a UAV (unmanned aerial vehicle) and a climbing inspection trolley. The UAV can autonomously load and unload the trolley on the OGW (overhead ground wires). The trolley can be separated from the flight mechanism and inspect the OGW finely and steadily. Outdoor experiments demonstrate the effectiveness of the system and approaches.

Secondly, for the tasks of UAV inspection for a tower and surrounding high-voltage power equipments, a visual SLAM (simultaneous localization and mapping) framework based on point-line features and semi-dense reconstruction is proposed. In this framework, a fast heuristic line feature extraction and matching method for the transmission tower structure is designed; The reconstruction process of line segments and the analytical expressions of Jacobian matrices for the optimization of point-line characteristics and UAV motion are derived; A method of GPS (global position system) data fusion and a semi-dense reconstruction approach are presented. The SLAM framework improves the accuracy of UAV positioning, the accuracy and the speed of environmental mapping and the ability to recover the detailed structures of tower. Simulation and field experiments verify the effectiveness of the proposed framework.

Thirdly, in order to select power line inspection images to improve the effectiveness of sample sets, a method based on subregion feature extraction for no-reference quality assessment of real degraded images is proposed. In this method, the problems of large differences among real degradations and no ground truths in subregions of an image are solved by the weighted prediction and focusing on the features; An inspection image quality labelling method and a loss function based on evaluation uncertainty are designed to solve the fluctuation problem of subjective evaluations. For the problems of large background interferences, slow speed and low accuracy when the general object detection method is applied to the detection of high-voltage power equipments, an efficient rotating bounding box detection method for multi-type power equipments is proposed. Experiments on the self-built datasets and the public dataset verify the effectiveness of the proposed approaches.

Fourthly, for the general image anomaly detection and the surface defect detection of the high-voltage power equipments and the OGW, a semi-supervised method based on feature extraction and image reconstruction is proposed. In this method, a conditional variational auto-encoder is derived and realized, makes the latent space become continuous and decoupled, enables the adaptive clustering and conditional generation; A method of maximizing mutual information based on feature discriminator is designed to solve the problem of distinguishing normal from anomalies when defect information is not clear; With a fine-grained reconstruction method which is based on the SSIM (structural similarity index) loss and the easily convergent GAN (generative adversarial network), an anomaly score to solve the problem of small surface defect detection is designed; An active negative training method combining the regularization degree in the latent space is designed. Experiments on the self-built datasets and the public datasets verify the effectiveness of the proposed approaches.

Finally, the thesis is summarized and the further works are pointed out.

Document Type学位论文
Corresponding Author边疆
Recommended Citation
GB/T 7714
边疆. 复合式机器人电力巡检与线路表观缺陷检测研究[D]. 中国科学院自动化研究所. 中国科学院大学,2020.
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