Autonomous landing on pipes using soft gripper for inspection and maintenance in outdoor environments

Abstract

The use of unmanned aerial systems for industrial applications has evolved considerably in recent years. This paper presents an aerial system capable of perching autonomously on pipes for inspection and maintenance in industrial environments. The target pipe to perch on is detected using a visual algorithm based on a semantic convolutional neuronal network. The information from a color camera is used to segment the image. Then, the segmentation information is fused with a depth image to estimate the pipe's pose, so that the pose of the robot can be controlled relative to it. The aerial robot is equipped with a soft landing system that robustly attaches it to the pipe. The article presents the complete development of the system. Experimental results performed in outdoor environments are shown.