Purpose
This paper aims to present an underwater climbing robot for wiping off marine life from steel pipes (e.g. jackets of oil platforms). The self-adaption mechanism that consists of a passive roll joint and combined magnet adhesion units provides the robot with better mobility and stability.
Design/methodology/approach
Adhesion requirements are achieved by analyses of falling and slipping. The movement status on pipes is analyzed to design the passive roll joint. The optimized structure parameters of the combined magnet adhesion unit are achieved by simulations. An approximation method is established to simplify the simulations conditions, and the simulations are conducted in two steps to save time effectively.
Findings
The self-adaption mechanism has expected performance that the robot can travel on pipes in different directions with high mobility. Meanwhile, the robot can clean continuous region of underwater pipes’ surface of offshore platforms.
Practical implications
The proposed underwater robot is needed by offshore oil platforms as their jackets require to be cleaned periodically. Compared with traditional maintenance by divers, it is more efficient, economic and safety.
Originality/value
Due to the specific self-adaption mechanism, the robot has good mobility and stability in any directions on pipes with different diameters. The good performance of striping attachments from pipes makes the underwater robot be a novel solution to clean steel pipes.
In this paper, we extended video stitching to nearshore bathymetry for videos that were captured for the same coastal field simultaneously by two unmanned aerial vehicles (UAVs). In practice, a video captured by a single UAV often shows a limited coastal zone with a lack of a wide field of view. To solve this problem, we proposed a framework in which video stitching and bathymetric mapping were performed in sequence. Specifically, our method listed the video acquisition strategy and took two overlapping videos captured by two UAVs as inputs. Then, we adopted a unified video stitching and stabilization optimization to compute the stitching and stabilization of one of the videos separately. In this way, we can obtain the best stitching result. At the same time, background feature points identification on the shore plays the role of short-time visual odometry. Through the obtained panoramic video in Shuang Yue Bay, China, we used the temporal cross-correlation analysis based on the linear dispersion relationship to estimate the water depth. We selected the region of interest (ROI) area from the panoramic video, performed an orthorectification transformation and extracted time-stack images from it. The wave celerity was then estimated from the correlation of the signal through filtering processes. Finally, the bathymetry results were compared with the cBathy. By applying this method to two UAVs, a wider FOV was created and the surveying area was expanded, which provided effective input data for the bathymetry algorithms.
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