Recent progress on underwater soft robots: adhesion, grabbing, actuating, and sensing

The research on biomimetic robots, especially soft robots with flexible materials as the main structure, is constantly being explored. It integrates multi-disciplinary content, such as bionics, material science, mechatronics engineering, and control theory, and belongs to the cross-disciplinary field related to mechanical bionics and biological manufacturing. With the continuous development of various related disciplines, this area has become a hot research field. Particularly with the development of practical technologies such as 3D printing technology, shape memory alloy, piezoelectric materials, and hydrogels at the present stage, the functions and forms of soft robots are constantly being further developed, and a variety of new soft robots keep emerging. Soft robots, combined with their own materials or structural characteristics of large deformation, have almost unlimited degrees of freedom (DoF) compared with rigid robots, which also provide a more reliable structural basis for soft robots to adapt to the natural environment. Therefore, soft robots will have extremely strong adaptability in some special conditions. As a type of robot made of flexible materials, the changeable pose structure of soft robots is especially suitable for the large application environment of the ocean. Soft robots working underwater can better mimic the movement characteristics of marine life in the hope of achieving more complex underwater tasks. The main focus of this paper is to classify different types of underwater organisms according to their common motion modes, focusing on the achievements of some bionic mechanisms in different functional fields that have imitated various motion modes underwater in recent years (e.g., the underwater sucking glove, the underwater Gripper, and the self-powered soft robot). The development of various task types (e.g., grasping, adhesive, driving or swimming, and sensing functions) and mechanism realization forms of the underwater soft robot are described based on this article.

Deciphering the connection between upstream obstacles, wake structures, and root signals in seal whisker array sensing using interpretable neural networks

This study presents a novel method that combines a computational fluid-structure interaction model with an interpretable deep-learning model to explore the fundamental mechanisms of seal whisker sensing. By establishing connections between crucial signal patterns, flow characteristics, and attributes of upstream obstacles, the method has the potential to enhance our understanding of the intricate sensing mechanisms. The effectiveness of the method is demonstrated through its accurate prediction of the location and orientation of a circular plate placed in front of seal whisker arrays. The model also generates temporal and spatial importance values of the signals, enabling the identification of significant temporal-spatial signal patterns crucial for the network's predictions. These signal patterns are further correlated with flow structures, allowing for the identification of important flow features relevant for accurate prediction. The study provides insights into seal whiskers' perception of complex underwater environments, inspiring advancements in underwater sensing technologies.

Design of an array of piezoresistive airflow sensors based on pressure loading mode for simultaneous detection of airflow velocity and direction

As an irreplaceable element for obtaining airflow information in many engineering scenarios, airflow sensors have gained increasing attention across the fields of aerospace engineering, environmental engineering, sustainable energy exploitation, meteorology research, and so on. As one of the mainstream airflow sensing principles, piezoresistive airflow velocity sensors have experienced rapid growth over the years, while effective vector airflow sensors with the ability of detecting both airflow velocity and direction based on the piezoresistive principle are scarce. Here, on the basis of our developed piezoresistive airflow velocity sensors based on pressure loading mode, we design an array of these sensors and propose a corresponding explicit algorithm for simultaneous detection of airflow velocity and direction. This sensor array configuration enables an automatic recognition function of the quadrant of incoming airflow, which can significantly simplify the reverse calculation of airflow information compared with conventional vector airflow sensors. The experimental results demonstrate the decent performance of this sensor array for identifying both airflow velocity and direction. This study not only fills the gap between our developed airflow velocity sensor and the ability of detecting airflow direction but also presents a simple and universal array-based strategy for vector airflow sensing, which could be widely applied in airflow sensors based on other principles.