Complex pipeline network should be inspected regularly for safety. In general, these tasks are often completed by large pipe-climbing robots or customized equipment. Most of them are not effective, and cannot work on pipes with uncertain barriers. Moreover, some pipes are mounted in constrained scenarios, so bulky robots are not applicable. This paper presents a tethered soft-rigid pipe-climbing robot to fill the gap. Two indispensable actions, i.e. embracing pipe and moving along it, are realized by a soft component and a 3D printed wheel mechanism. The latter includes two forces: thrust force from the compressed air and tractive force from wheels, to drive the robot comprehensively, and a lightweight body (only 160g) benefits agile motion. In operation, pressure exerted on the soft component enables the robot to embrace pipes of different diameters, with controllable adhesion force, and locomotion force is also regulated. Inspired from vehicle, an elastic damper is attached between the wheel structure and the robot body, which can effectively alleviate vibration when crossing barriers. In addition, theoretical models are constructed to analyse and control thrust force, and the locomotion performance is analysed by dynamics model. Experiments demonstrate that this robot can perform rapid climbing at a speed of 1.09m/s in load-free scenarios, and it can move at a maximum speed of 0.828m/s with 500g load. Reconstruction of a flexible pipe using the robot is also demonstrated.

Knowledge Graphs (KGs) are widely used in various applications of information retrieval. Despite the large scale of KGs, they are still facing incomplete problems. Conventional approaches on Knowledge Graph Completion (KGC) require a large number of training instances for each relation. However, long-tail relations which only have a few related triples are ubiquitous in KGs. Therefore, it is very difficult to complete the long-tail relations. In this paper, we propose a meta pattern learning framework (MetaP) to predict new facts of relations under a challenging setting where there is only one reference for each relation. Patterns in data are representative regularities to classify data. Triples in KGs also conform to relation-specific patterns which can be used to measure the validity of triples. Our model extracts the patterns effectively through a convolutional pattern learner and measures the validity of triples accurately by matching query patterns with reference patterns. Extensive experiments demonstrate the effectiveness of our method. Besides, we build a few-shot KGC dataset of COVID-19 to assist the research process of the new coronavirus.