Applied Sciences, Volume 14, Issue 3
2024 February-1 - 371 articles
Cover Story: Despite rapid strides in robot-control technology, controlling robots underwater remains a formidable challenge, primarily due to the complex behavior of fluids. In an innovative approach, we are turning to deep learning to unravel these fluid dynamics. With multi-layer perceptron, by observing how robots move both in the air and underwater, we can understand the intricate interaction between the robot and the water, focusing on differences in the torque of each joint. This approach holds promise for improved performance of underwater motion control, enabling more precise predictions of torque generation that are crucial for optimizing the movement of underwater robots. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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