An automatic precision robot assembly system is established. The robot assembly system mainly consists of an industrial robot, three cameras, a micro force sensor, and a specific gripper. The industrial robot is a six-axis serial manipulator, which is used to conduct grasping and assembly subtasks. Two microscopic cameras are fixed on two high accuracy translational platforms to provide visual information in aligning stage for assembly. While one conventional camera is installed on the robotic end effector to guide the gripper to grasp component. The micro force sensor is installed on the robotic end effector to perceive the contacted forces in inserting stage. According to the characteristics of components, an adsorptive gripper is designed to pick up components. In addition, a three-stage “aligning–approaching–grasping” control strategy for grasping subtask and a two-stage “aligning–inserting” control strategy for assembly subtask are proposed. Position offset compensation is computed and introduced into aligning stage for assembly to make the grasped component in the microscopic cameras’ small field of view. Finally, based on the established robot assembly system and the proposed control strategies, the assembly tasks including grasping and assembly are carried out automatically. With 30 grasping experiments, the success rate is 100%. Besides, the position and orientation alignment errors of pose alignment for assembly are less than 20 μm and 0.1°.

This paper proposes an efficient robot assembly skill learning framework based on only a few demonstrations. The assembly skill learning process consists of two phases, e.g., the pre-training phase and the self-learning phase. In pre-training phase, the assembly networks are initialized from demonstration data. A novel data augmentation model based on state transition model is designed, which only requires several human demonstrations to learn the parameters of the model and reduces the cost of demonstration data collection in pre-training. In self-learning phase, the pre-trained assembly networks are further optimized by a hybrid exploration strategy in assembly environment, which explores both parameter space and action space to increase exploration efficiency. On par with the learning framework, a fuzzy reward function balancing the efficiency and compliance of assembly is elaborately designed to evaluate action's performance in assembly skill self-learning process. Series of physical experiments were well conducted on a sophisticated assembly platform to verify the effectiveness of the proposed efficient robot assembly skill learning framework. Experimental results demonstrate that the learning efficiency of the proposed framework is at least three times as efficient as the state-of-art methods, while the skill performance outperforms the state-of-art by more than 50%.

In this paper, a multilayer ultra-wideband transparent metamaterial wave absorber is proposed, which has the characteristics of ultra-wideband wave absorption, light transmission and flexible bending; in addition, due to the complete symmetry of the structure, the absorber has polarization insensitivity to incident electromagnetic waves. Both simulation and experimental results show that the frequency range of the microwave absorption rate is higher than 90% between 8.7 GHz and 38.9 GHz (between which most of the absorption rate can reach more than 95%), the total bandwidth is 30.2 GHz, and the relative bandwidth is 126.9%, realizing microwave broadband absorption and covering commonly used communication frequency bands such as X-band, Ku-band, and K-band. A sample was processed and tested. The test results are in good agreement with the results of the theoretical analysis, which proves the correctness of the theoretical analysis. In addition, through the selection and oxidation of indium tin (ITO) materials, the metamaterial also has the characteristics of optical transparency and flexibility, so it has potential application value in the window radar stealth and conformal radar stealth of weapons and equipment.

In this paper, a new metasurface structure is designed to reduce radar cross section (RCS). In order to reduce the radar cross section (RCS), a new metasurface structure is designed. The unit adopts the polarization conversion metasurface structure, which is composed of asymmetric double arrow-shaped metal structure and etched on the dielectric substrate on the back of the metal plate. This polarization conversion metasurface (PCM) unit consists of asymmetric double arrow-shaped metal structure, and it is etched on a dielectric substrate backed with metallic plate. It can realize converting linear polarized waves to orthogonal ones in a broadband from 18.1GHz to 32.7GHz efficiently. According to the further development of the triangle chessboard layout, we also designed several new circular fan-shaped configurations, which realize RCS reduction in a wider -10dB bandwidth compared with the triangle chessboard configuration. Finally, an optimized 8-lobed circular sector configuration composed of the designed PCM unit is selected for processing and testing. It can realize the RCS reduction greater than 10dB in a wide bandwidth ranging from 13.7GHz to 27.5GHz, compared with the equal-sized metallic-backed dielectric substrate plate. The experimental results basically agree with the simulation ones. The proposed broadband circular sector structure has broad application prospects such as phased array antenna technology, electromagnetic stealth technology, and electromagnetic absorption technology.