Marine mammals relying on tactile perception for hunting are able to achieve a remarkably high prey capture rate without visual or acoustic perception. Here, a self-powered triboelectric palm-like tactile sensor (TPTS) is designed to build a tactile perceptual system for underwater vehicles. It is enabled by a three-dimensional structure that mimics the leathery, granular texture in the palms of sea otters, whose inner neural architecture provides additional clues indicating the importance of tactile information. With the assistance of palm structure and triboelectric nanogenerator technology, the proposed TPTS has the ability to detect and distinguish normal and shear external load in real-time and approximate the external stimulation area, especially not affected by the touch frequency, that is, it can maintain stable performance under high-frequency contact. The results show that the TPTS is a promising tool for integration into grippers mounted on underwater vehicles to complete numerous underwater tasks.

In this paper we extend earlier results on Hosoya entropy (H-entropy) of graphs, and establish connections between H-entropy and automorphisms of graphs. In particular, we determine the H-entropy of graphs whose automorphism group has exactly two orbits, and characterize some classes of graphs with zero H-entropy.

Towing is a widely used mode of transportation in offshore engineering, and towing of unpowered platforms is of particular significance. However, the addition of unpowered facilities has increased the difficulty of ship maneuvering. Moreover, the marine environment is complex and changeable, and sudden winds or waves can have unpredictable effects on the towing process. Therefore, it is of great significance to overcome the influence of the harsh marine environment while navigating the towing system following a planned course to a target sea area. To tackle the time-varying disturbances, a course control method for a towing system of unpowered cylindrical drilling platform is designed based on double deep Q-network (DQN) optimized linear active disturbance rejection control (LADRC). To be specific, to tackle the difficulty of LADRC tuning, double DQN is applied to select the best parameters of the LADRC at any time according to the states of the system, without relying on the specific information of the model and the controller. The course control performance of the towing system is evaluated in a simulation environment under various disturbances. Moreover, the Monte Carlo experiment is used to test the robustness of the controller when the ship's mass changes and the robustness of the proposed method is verified by testing with various heading angles. The results show that the LADRC with adaptive parameters optimized by double DQN performs well under external interference and inherent uncertainty, and compared with the traditional LADRC, the proposed method has better course control effects.

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