Flexible sensing tends to be widely exploited in the process of human–computer interactions of intelligent robots for its contact compliance and environmental adaptability. A novel flexible capacitive tactile sensor was proposed for multi-directional force sensing, which is based on carbon black/polydimethylsiloxane (PDMS) composite dielectric layer and upper and lower electrodes of carbon nanotubes/polydimethylsiloxane (CNTs/PDMS) composite layer. By changing the ratio of carbon black, the resolution of carbon black/PDMS composite layer increases at 4 wt%, and then decreases, which was explained according to the percolation theory of the conductive particles in the polymer matrix. Mathematical model of force and capacitance variance was established, which can be used to predict the value of the applied force. Then, the prototype with carbon black/PDMS composite dielectric layer was fabricated and characterized. SEM observation was conducted and a ratio was introduced in the composites material design. It was concluded that the resolution of carbon sensor can reach 0.1 N within 50 N in normal direction and 0.2 N in 0–10 N in tangential direction with good stability. Finally, the multi-directional force results were obtained. Compared with the individual directional force results, the output capacitance value of multi-directional force was lower, which indicated the amplitude decrease in capacity change in the normal and tangential direction. This might be caused by the deformation distribution in the normal and tangential direction under multi-directional force.

In order to assist patients with finger rehabilitation training and grasping objects, we propose a new type of soft rehabilitation gloves (SRGs), which has both flexion/extension and abduction/adduction movement function for every finger. This paper describes the structure design of the bending actuator and rotating actuator, the fabrication process of the soft actuator, and the implementation of the soft wearable gloves based on a fabric glove. FEM simulation analysis and experiments were conducted to characterize the mechanical behavior and performance of the soft glove in terms of the angle output and force output upon pressurization. To operate this soft wearable glove, we designed the hardware system for SRGs with a flexible strain sensor and force sensor in the loop and introduced a force/position hybrid PID control algorithm to regulate the pressure inputted. Experiment evaluation focused on rehabilitation training gestures; motions and the precise grasping assistance function were executed. The rotating actuator between each finger can supply abduction/adduction motion manner for patients, which will improve rehabilitation effect. The experimental results demonstrated that the developed SRGs have the potential to improve hand movement freedom and the range of grasping successfully.

Accurately modeling the system dynamics of autonomous underwater vehicles (AUVs) is imperative to facilitating the implementation of intelligent control. In this research, we introduce a physics-informed neural network (PINN) method to model the dynamics of AUVs by integrating dynamical equations with deep neural networks. This integration leverages the nonlinear expressive power of deep neural networks, alongside the robust foundation of physical prior knowledge, resulting in an AUV model proficient in long-term motion forecasting. The experimental results indicate that this method is capable of effectively extracting AUV system dynamics from datasets, exhibiting strong generalization capabilities and achieving robust long-term motion prediction. Furthermore, a model predictive control method is proposed, using the learned PINN as the predictive model to accurately track the closed-loop trajectory. This research offers novel perspectives on the dynamics modeling of AUVs and has the potential to be applied in other relevant research endeavors.

With the superior ductility and flexibility brought by compliant bodies, soft manipulators provide a nondestructive manner to grasp delicate objects, which has been developing gradually as a rising focus of soft robots. However, the unexpected phenomenon caused by environmental effects, leading to high internal nonlinearity and unpredictable deformation, makes it challenging to design, model, and control soft manipulators. In this paper, we designed a soft pneumatically actuated manipulator consisting of four soft actuators, as well as a flange, and investigated the influence of structural parameters on the output characteristics of the manipulator through finite element analysis (FEA). To enhance the bending deformation of the soft actuator, annular rings were employed on the soft actuator. A mathematical model for the bending deformation of air cavities was established to explore the relationship between the driving pressure and the bending angle based on the Yeoh strain energy function. Moreover, an end-output force model was established to depict the variation of the force output with the bending angle of the soft actuator, which was then experimentally validated by adopting the manufactured manipulator. The soft actuator studied in this paper can bend from 0° to 110° under an applied pressure of 0–60 kPa, and the maximum grasping load of the soft manipulator is 5.8 N. Finally, practical tests were conducted to assess the adaptability of the soft manipulator when grasping delicate fruits, such as apples, pears, tomatoes, and mangoes, demonstrating its broad application prospects in nondestructive fruit harvesting.

Detecting and classifying the plankton in situ to analyze the population diversity and abundance is fundamental for the understanding of marine planktonic ecosystem. However, the features of plankton are subtle, and the distribution of different plankton taxa is extremely imbalanced in the real marine environment, both of which limit the detection and classification performance of them while implementing the advanced recognition models, especially for the rare taxa. In this paper, a novel plankton detection strategy is proposed combining with a cycle-consistent adversarial network and a densely connected YOLOV3 model, which not only solves the class imbalanced distribution problem of plankton by augmenting data volume for the rare taxa but also reduces the loss of the features in the plankton detection neural network. The mAP of the proposed plankton detection strategy achieved 97.21% and 97.14%, respectively, under two experimental datasets with a difference in the number of rare taxa, which demonstrated the superior performance of plankton detection comparing with other state-of-the-art models. Especially for the rare taxa, the detection accuracy for each rare taxa is improved by about 4.02% on average under the two experimental datasets. Furthermore, the proposed strategy may have the potential to be deployed into an autonomous underwater vehicle for mobile plankton ecosystem observation.

In challenging tasks such as large-scale resource detection, deep-sea exploration, prolonged cruising, extensive topographical mapping, and operations within intricate current regions, AUV swarm technologies play a pivotal role. A core technical challenge within this realm is the precise determination of relative positions among AUVs within the cluster. Given the complexity of underwater environments, this study introduces an integrated and high-precision underwater cluster positioning method, incorporating advanced image restoration algorithms and enhanced underwater visual markers. Utilizing the Hydro-Optical Image Restoration Model (HOIRM) developed in this research, image clarity in underwater settings is significantly improved, thereby expanding the attenuation coefficient range for marker identification and enhancing it by at least 20%. Compared to other markers, the novel underwater visual marker designed in this research elevates positioning accuracy by 1.5 times under optimal water conditions and twice as much under adverse conditions. By synthesizing the aforementioned techniques, this study has successfully developed a comprehensive underwater visual positioning algorithm, amalgamating image restoration, feature detection, geometric code value analysis, and pose resolution. The efficacy of the method has been validated through real-world underwater swarm experiments, providing crucial navigational and operational assurance for AUV clusters.