Carbon fiber-reinforced composites are widely used in automobile, aerospace and military lightweight manufacturing due to their excellent mechanical properties such as light weight, excellent fracture resistance, corrosion resistance and wear resistance, etc. However, because of their high hardness, anisotropy and low interlayer strength characteristics, there are many problems with machine carbon fiber-reinforced composites with traditional methods. As a non-contact processing technology, laser machining technology has lots of advantages in carbon fiber-reinforced composites processing. However, there are also some defects produced in laser machining process such the heat affected zone, delamination and fiber extraction due to the great difference of physical properties between the carbon fibers and the resin matrix. To improve the quality of carbon fiber-reinforced composites laser machining, lots of works have been carried out. In this paper, the research progress of carbon fiber-reinforced composites laser machining parameters optimization and numerical simulation was summarized, the characteristics of laser cutting carbon fiber-reinforced composites and cutting quality influence factors were discussed, and the developing trend of the carbon fiber-reinforced composites laser cutting was prospected.

Carbon fiber reinforced thermosetting composites (CFRTS) and TC4 alloy are important structural materials for lightweight manufacturing. The hybrid structure of these two materials has been widely used in the aerospace field. However, the CFRTS-TC4 alloy joint formed by the traditional connection method has many challenges, such as poor environmental adaptability and stress concentration. Laser micro-texturing of metal surface-assisted laser connection of CFRTS and TC4 alloy has great potential in improving joint strength. In order to study the effect of laser micro-texturing on the laser bonding of CFRTS and TC4 alloy, the simulation and experimental research of laser welding of TC4 alloy and CFRTS based on laser micro-textures with different scanning spacings were carried out, and the interface hybrid pretreatment method of laser cleaning and laser plastic-covered treatment was introduced to assist the high-quality laser bonding of heterogeneous joints. The results showed that the established finite element model of CFRTS-TC4 alloy laser welding can predict the temperature field distribution of the joint during the welding process and reflect the forming mechanism of the joint. The laser micro-textures with different scanning spacings will lead to a difference in the temperature field distribution on the polyamide (PA6) interface, which leads to a change in heat input on the CFRTS surface. When the laser scanning spacing is 0.3 mm, the joint strength can reach 14.3 MPa. The failure mechanism of the joint mainly includes the cohesive failure of the internal tear of the carbon fiber and the interfacial failure of the interface between the PA6 resin and the TC4 alloy.

To reduce manual operation and enhance the intelligence of the high-altitude maintenance wall-climbing robot during its operation, path planning and autonomous navigation need to be implemented. Due to non-uniform magnetic adhesion between the wall-climbing robot and the steel plate, often caused by variations in steel thickness or surface pitting, the wall-climbing robot may experience motion deviations and deviate from its planned trajectory. In order to obtain the actual deviation from the expected trajectory, it is necessary to accurately locate the wall-climbing robot. This allows for the generation of precise control signals, enabling trajectory correction and ensuring high-precision autonomous navigation. Therefore, this paper proposes an external visual localization system based on a pan–tilt laser tracker unit. The system utilizes a zoom camera to track an AprilTag marker and drives the pan–tilt platform, while a laser rangefinder provides high-accuracy distance measurement. The robot’s three-dimensional (3D) pose is ultimately calculated by fusing the visual and ranging data. However, due to the limited tracking speed of the pan–tilt mechanism relative to the robot’s movement, we introduce an Extended Kalman Filter (EKF) to robustly predict the robot’s true spatial coordinates. The robot’s three-dimensional coordinates are periodically compared with the predefined route coordinates to calculate the deviation. This comparison generates closed-loop control signals for the robot’s movement direction and speed. Finally, based on the LoRa communication protocol, closed-loop control of the robot’s movement direction and speed are achieved through the upper-level computer, ensuring that the robot returns to the predefined track. Extensive comparative experiments demonstrate that the localization system achieves stable localization with an accuracy better than 0.025 m on a 6 m × 2.5 m steel structure surface. Based on this high-precision positioning and motion correction, the robot’s motion deviation is kept within 0.1 m, providing a reliable pose reference for precise motion control and high-reliability operation in complex structural environments.

Magnetic climbing robots hold significant value for operations in complex industrial environments, particularly for the inspection and maintenance of large-scale metal structures. High-precision spatial positioning is the foundation for enabling autonomous and intelligent operations in such environments. However, the existing literature lacks a systematic and comprehensive review of spatial positioning techniques tailored to magnetic climbing robots. This paper addresses this gap by categorizing and evaluating current spatial positioning approaches. Initially, single-sensor-based methods are analyzed with a focus on external sensor approaches. Then, multi-sensor fusion methods are explored to overcome the shortcomings of single-sensor-based approaches. Multi-sensor fusion methods include simultaneous localization and mapping (SLAM), integrated positioning systems, and multi-robot cooperative positioning. To address non-uniform noise and environmental interference, both analytical and learning-based reinforcement approaches are reviewed. Common analytical methods include Kalman-type filtering, particle filtering, and correlation filtering, while typical learning-based approaches involve deep reinforcement learning (DRL) and neural networks (NNs). Finally, challenges and future development trends are discussed. Multi-sensor fusion and lightweight design are the future trends in the advancement of spatial positioning technologies for magnetic climbing robots.