Artificial intelligence (AI) is profoundly reshaping the technological framework of industrial robotics, driving its transition from pre-programmed automation to autonomous, adaptive agents. This paper systematically reviews the key advancements of AI across three core dimensions of intelligence: perception, decision-making, and execution. Analysis indicates that AI is propelling industrial robots from tools executing predefined tasks towards intelligent partners capable of adapting to unstructured environments, autonomously planning amid dynamic changes, and engaging in nuanced interactions with the physical world. This evolution reveals a shift from optimizing specific skills towards developing rudimentary task-level cognitive reasoning capabilities. Nevertheless, fundamental challenges persist for industrial-scale deployment, including model generalization capabilities, long-term robustness, and human-machine trust. Collectively, these advancements are shaping a new generation of intelligent industrial robotic systems that are more adaptable and capable of deeper collaboration with humans.

Underground engineering is becoming increasingly important in modern urban construction and mine development. However, the shape of underground roadways may deform elastically or plastically due to geological conditions and accident loads, a phenomenon that cannot be ignored. Therefore, this paper proposes a roadway deformation detection method based on laser scanning. First, the working principle of the point cloud denoising and downsampling method is explained. To overcome the limitations of this method, the paper presents a point cloud denoising approach that combines statistical and median filtering. Additionally, it introduces a voxelised grid-downsampling technique based on density constraints and the centre of gravity. Next, the bidirectional projection method is used to determine the roadway’s central axis. Then, CloudCompare point cloud processing software is used to segment the point cloud, extract the roadway section, and fit a contour curve. Finally, the methods for extracting roadway deformation from processed point cloud data and for detecting and analysing it are introduced. Experiments on roadway deformation detection are conducted on an inspection robot experimental platform to verify the feasibility of the overall scheme. Experimental results indicate that the measurement error of light detection and ranging scanning for tunnel contour is less than 2 mm.

The bionic inchworm robot is known for its flexible and adaptable locomotion and has attracted growing interest in agriculture, forestry, and infrastructure inspection. This paper reviews global research on such robots, focusing on actuation mechanisms, attachment strategies, kinematic modeling, control methods and locomotion performance. By systematically comparing existing studies, it summarizes key technologies, identifies current challenges and outlines future research directions. The goal is to provide a clear perspective that supports further advances in inchworm-inspired robotic systems.

The ionosphere plays a crucial role in the transmission and propagation of space signals. As a component of the upper atmosphere, it exhibits distinct spatio-temporal variations and is influenced by solar and geomagnetic activities. Accurately modeling and predicting the ionosphere remains a significant challenge. Recent advancements in deep learning techniques have provided valuable insights into these challenges, offering new approaches for spatio-temporal ionospheric modeling and prediction. By integrating multiple observations from both space-borne and ground-based stations, high-resolution digital models of the ionosphere can be constructed using convolutional and recurrent neural networks. This paper reviews the recent progress in ionospheric modeling and prediction using deep learning networks, discusses the advantages of deep learning models over traditional empirical models, and outlines future directions to address the remaining challenges in this field.

This study presents an improved model predictive control (MPC) approach for unmanned underwater vehicle trajectory tracking, specifically in an environment with ocean current disturbance. The proposed control strategy consists mainly of two MPC frameworks. Each MPC framework additionally attaches a nonlinear constraint used to further optimize the results. The constraint of the first part uses the Lyapunov direct method, while the constraint in the second part is based on the adaptive sliding mode controller, which has a decisive impact on the performance of the whole controller. These constraints give the system the ability to optimize the force output, increase the robustness, and reduce the tracking error. To evaluate the performance of the proposed controller, simulation experiments are conducted, comparing it with commonly used controllers. The results show the characteristics of the proposed method, including stability in the presence of undetectable disturbances and the advantage of effectively mitigating thrust saturation and oscillation caused by motion coupling.