Jellyfish-Inspired Soft Robot Driven by Pneumatic Bistable Actuators.

Soft actuators offer numerous potential applications; however, challenges persist in achieving a high driving force and fast response speed. In this work, we present the design, fabrication, and analysis of a soft pneumatic bistable actuator (PBA) mimicking jellyfish subumbrellar muscle motion for waterjet propulsion. Drawing inspiration from the jellyfish jet propulsion and the characteristics of bistable structure, we develop an elastic band stretch prebending PBA with a simple structure, low inflation cost, exceptional driving performance, and stable driving force output.

Bioinspired soft robots for deep-sea exploration.

The deep ocean, Earth's untouched expanse, presents immense challenges for exploration due to its extreme pressure, temperature, and darkness. Unlike traditional marine robots that require specialized metallic vessels for protection, deep-sea species thrive without such cumbersome pressure-resistant designs. Their pressure-adaptive forms, unique propulsion methods, and advanced senses have inspired innovation in designing lightweight, compact soft machines.

A fault diagnostic approach based on PSO-HMM for underwater thrusters.

In this paper, we describe an approach based on improved Hidden Markov Model (HMM) for fault diagnosis of underwater thrusters in complex marine environments. First, considering the characteristics of thruster data, we design a three-step data preprocessing method. Then, we propose a fault classification method based on HMMs trained by Particle Swarm Optimization (PSO) for better performance than methods based on vanilla HMMs.

Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network.

In this article, a novel thruster information fusion fault diagnosis method for the deep-sea human occupied vehicle (HOV) is proposed. A deep belief network (DBN) is introduced into the multisensor information fusion model to identify uncertain and unknown, continuously changing fault patterns of the deep-sea HOV thruster. Inputs for the DBN information fusion fault diagnosis model are the control voltage, feedback current, and rotational speed of the deep-sea HOV thruster; and the output is the corresponding fault degree parameter ( s ), which indicates the pattern and degree of the thruster fault.

High-precision initialization and acceleration of particle filter convergence to improve the accuracy and stability of terrain aided navigation.

Initial positioning errors and the low adaptability of a priori digital elevation maps result in large positioning uncertainty intervals in the initial stage of terrain-aided navigation (TAN). This produces pseudo-peaks and mismatches in the initial position likelihood function and renders the convergence of the particle filter (PF) slow and unstable, while even causing divergence. Thus, the occurrence of the "kidnapped robot problem" is highly probable during the initial stage of TAN and is a scenario frequently faced by deep-sea and ultra-long-range underwater vehicles.

A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering.

Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results.