In order to adhere to the wall stably in an underwater environment, a vortex suction cup that injects high-pressure water inside via two axisymmetrically side-distributed inlets to create a negative pressure area in the center is the necessary component for the underwater climbing robot (UCR). However, the suction force of this vortex suction cup is reduced and periodically unstable due to unstable cavitation. The aim of this paper is to propose a cavitation reduction optimization method for vortex suction cups and to verify the effectiveness of the optimization. Analyses of this vortex flow, including streamlines, pressure, and cavitation number fluctuations, were carried out by the introduced computational fluid dynamics (CFD) simulating methods based on the multiphase RNG k−ε model to study the periodic fluctuations of the suction force of the original suction cup and the optimized ones. Force measurement and vortex observation experiments were conducted to compare the suction force of the original vortex suction cup and the optimized suction cup, as well as the cavitation and pressure fluctuation phenomenon. Results of simulation and experiments prove the existence of the effect of vortex cavitation on the suction performance and verify the rationality of optimization as well.
Due to the particularity of the jacket structure of offshore platforms and the complexity of the marine environment, there have been few effective localization and autonomous control methods for underwater robots that are designed for cleaning tasks. To improve this situation, a fusion bat algorithm (BA) online optimized fuzzy control method using vision localization was developed based on the constraints of the underwater operational environment. Vision localization was achieved based on images from a catadioptric panoramic imaging system. The features of the pipe edge and the boundary of the area covered by biofouling were obtained by image processing and feature extraction. The feature point chosen as the “highest” point of the boundary was calculated by projection transformation to generate the reference path. The specialized fuzzy controller was designed to drive the robot to track the reference path, and an improved bat algorithm with dynamic inertia weight and differential evolution method was developed to optimize the scale factors of the fuzzy controller online. The control method was simulated and further implemented on an underwater pipe-cleaning robot (UPCR), and the results indicate its rationality and validity.
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