Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.

Droplet generation process can directly affect process regulation and output performance of electrohydrodynamic jet (E-jet) printing in fabricating micro-to-nano scale functional structures. This paper proposes a numerical simulation model for whole process of droplet generation of E-jet printing based on the Taylor-Melcher leaky-dielectric model. The whole process of droplet generation is successfully simulated in one whole cycle, including Taylor cone generation, jet onset, jet break, and jet retraction. The feasibility and accuracy of the numerical simulation model is validated by a 30G stainless nozzle with inner diameter ~160 μm by E-jet printing experiments. Comparing numerical simulations and experimental results, period, velocity magnitude, four steps in an injection cycle, and shape of jet in each step are in good agreement. Further simulations are performed to reveal three design constraints against applied voltage, flow rate, and nozzle diameter, respectively. The established cone-jet numerical simulation model paves the way to investigate influences of process parameters and guide design of printheads for E-jet printing system with high performance in the future.

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as $B=f\left(A\right)$, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.

The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic cylinder. In this paper, a model is proposed to simulate a small internal leakage of hydraulic cylinders, to convert the amount of leakage of hydraulic oil into strain signals through high-precision strain gauges and to train the collected strain signals using various neural networks to form a computational model and obtain prediction results from the model. The neural networks applied in this paper are convolutional neural networks, BP neural networks, T-S neural networks and Elman neural networks. The predicted results of the neural network are compared with the actual leakage amount. The results show that the prediction accuracy of the above four kinds of neural networks are all above 90%, of which the convolutional neural network is the most accurate. This research provides scientific and technical support for measuring and predicting small leaks.

In this paper, the interaction mechanism between the solid–liquid–gas interface phenomenon caused by nanostructure and surface wettability and boiling heat transfer is described, and the heat transfer theory of single wettable nanostructure surface and mixed wettable nanostructure surface is proposed. Through molecular dynamics simulation, the thermodynamic model of the wettable surface of nanostructures is established. The nanostructures are set as four rectangular lattice structures with a height of 18 Å. The solid atoms are platinum atoms, and the liquid atoms are argon atoms. The simulation results show that with the increase of surface hydrophilicity of nanostructures, the fluid temperature increases significantly, and the heat transfer at the interface is enhanced. With the increase in surface hydrophobicity of nanostructures, the atoms staying on the surface of nanostructures are affected by the hydrophobicity, showing a phenomenon of exclusion, and the evaporation rate in the evaporation area of nanostructures is significantly increased. In addition, the mixed wettable surface is influenced by the atomic potential energy and kinetic energy of the solid surface, and when compared with the pure wettable surface under the nanostructure, it changes the diffusion behavior of argon atoms on the nanostructure surface, enhances the heat transfer phenomenon compared with the pure hydrophobic surface, and enhances the evaporation phenomenon compared with the pure hydrophilic surface. This study provides insights into the relationship between the vapor film and the heating surface with mixed wettability and nanostructures.