In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

Tuning of a proportional-integral-derivative (PID) controller for a complex multi-joint structure, such as an exoskeleton, using conventional methods is difficult and imprecise. In this paper, an optimal PID tuning method for a 3-dimensional model of a lower-limb human exoskeleton in gait training condition is presented. The dynamic equation of the human-exoskeleton is determined using a Lagrangian approach, and its transfer function is established in a closed-loop control system. PID controller gains, initialized by the Ziegler–Nichols (Z-N) method, are used as the input to an adaptive particle swarm optimization (APSO) algorithm for minimizing the multi-joint trajectory error. The optimized controller is tested in the Gazebo virtual environment and compared with the Z-N and conventional optimization methods. The numerical analysis shows that the PID controller tuned by a combination of Z-N and APSO improves the performance of a lower-limb human exoskeleton in gait training.

With advancements in technology, deep learning has become increasingly widespread, particularly in fields like robot control, computer vision, and autonomous driving. In these areas, obtaining pose information of target objects, especially their spatial location, is crucial for robot grasping tasks. Although many effective implementations of six degrees of freedom (6D) pose estimation methods based on RGB images exist, challenges in this domain persist. This paper provides a comprehensive review of traditional 6D pose estimation methods, deep learning approaches, and point cloud techniques by analyzing their advantages and disadvantages. It also discusses evaluation metrics and performance on common datasets for 6D pose estimation. Furthermore, the paper offers a theoretical foundation for robot grasping and explores future directions for 6D pose estimation. Finally, it summarizes the current state and development trends of 6D pose estimation, aiming to help researchers better understand and learn about this field.

This paper comprehensively reviews advanced signal processing methods for partial discharge (PD) analysis, covering traditional time-frequency techniques, wavelet transform, Hilbert–Huang transform, and artificial intelligence-based methods. This paper critically examines the principles, advantages, limitations, and applicable scenarios of each method. A key contribution of this review is the systematic comparison of these methods, highlighting their evolution and complementary roles in processing non-stationary and noisy PD signals. However, a significant gap in current research remains the lack of standardized, explainable, and embeddable AI solutions for real-time, fine-grained PD classification. Future trends point to hybrid approaches and edge AI systems that combine physical insights with lightweight deep learning models to improve diagnostic accuracy and deployability.

In a complex and changing agricultural environment, achieving autonomous navigation of agricultural robots and field machinery is a fundamental prerequisite for completing various farming tasks. However, because of the unstructured nature, high dynamics, and signal interference commonly found in agricultural scenarios, achieving high-precision autonomous navigation in such challenging environments remains a significant research challenge. In response to the unique complexity of agricultural settings, this study outlines the evolution of agricultural navigation technologies from single-sensor systems to advanced multi-sensor approaches and systematically evaluates the applicability of commonly used single-sensor methods in complex field environments. The findings indicate that, among single-sensor navigation applications, vision-based techniques are more widely adopted in agricultural scenarios. In contrast, for multi-sensor navigation, integrated systems combining inertial navigation systems and global navigation satellite systems are the most prevalent. Furthermore, in terms of multi-sensor fusion algorithms, kalman filters and their extended variants are extensively employed, demonstrating strong robustness and adaptability. These analyses can offer valuable theoretical foundations and practical guidance for the selection and optimization of fusion strategies in future agricultural navigation systems. This study also compared global and local path planning algorithms. Finally, practical applications of fusion technologies in various agricultural scenarios are discussed.