In recent years, underwater robots have been increasingly used in the maintenance of hydraulic structures. Underwater manipulators are essential devices that are used to carry out such maintenance tasks. For delicate repair operations such as fixing tiny cracks, most existing underwater manipulators face limitations in terms of size, accuracy, and scalability. Therefore, in this letter, we present a novel electric underwater manipulator, named SEU-4. This four-degree-of-freedom manipulator weighs 8.91 kg and has a maximum payload of 9 kg. It has a rapid-switching interface that supports convenient mechanical and electrical connections for end-effectors. To compensate for the disturbances that are present in the complex underwater environment, a trajectory-tracking control strategy based on a disturbance observer and sliding-mode control (DOB-SMC) is proposed. A prototype of the proposed underwater manipulator was created, and a flowing-water experimental platform was constructed to test its trajectory-tracking performance in fast-flowing water. The experimental results show that the manipulator achieves a trajectory-tracking error of 1.03 mm in static water and 2.91 mm in flowing water at 1.2 m/s, which satisfies the requirements of delicate repair operations.

Overhead aluminum conductor composite core (ACCC) transmission lines are extensively used. However, internal defects of ACCC wires are difficult to detect, threatening the stability and security of the grid. Thus, a novel automatic detection system using an X-ray inspection robot and an anchor-free object detection model is proposed to solve the problem of detecting internal defects in ACCC wires. First, a new inspection robot with a nondestructive testing (NDT) system consisting of a digital radiography (DR) detection panel and a portable X-ray generator is developed to acquire X-ray images of ACCC wires. Then, the IN-ACCC dataset is created by collecting the X-ray images of artificial defective ACCC wires and then processing, classifying, and labeling the images. Finally, an anchor-free object detection model named CenterNet-NDT is proposed based on CenterNet for high-performance identification of internal defects. CenterNet-NDT has a specially designed feature fusion module composed of SPPCSPC, polarized self-attention (PSA), and a newly weighted bidirectional feature pyramid network named SOFPN. Compared with some state-of-the-art methods and CenterNet with different modules, the proposed CenterNet-NDT achieves the highest mAP of 90.60% on the IN-ACCC dataset. The proposed automatic internal defect detection system is verified to be effective and robust by lab experiments and has been repeatedly applied in actual ACCC transmission line inspection tasks to reduce the safety hazards of wire breakage.

Over the past two decades, the number of people with arm disabilities has dramatically increased. Many researchers have begun to concentrate on developing technologies to address this challenge. Exoskeleton is one of the most successful technologies out of these since it does not require medical staff to accompany the patients and can continually rehabilitate the patients until they regain the ability to move voluntarily. However, the majority of exoskeletons are anchored to the ground and individuals are unable to purchase one due to its high manufacturing costs. Furthermore, since this type of exoskeleton is anchored to the ground, patients must go to hospitals to get rehabilitation services. In this study, a portable assistive exoskeleton for human arm motions is designed to address this problem. This device can carry patients' arms to the desired position by adhering to a prescribed trajectory by medical specialists. The main parts of this exoskeleton are made of polylactic acid and produced using 3D printing technology. Therefore, the total manufacturing costs of the exoskeleton are not excessive and the weight of it is not high as well.

During the inspection and maintenance of the underwater part of hydraulic structures, it is often necessary to clean the surface of a certain area for subsequent operations. At present, there are still few robots capable of underwater fine cleaning. Therefore, this letter introduces the design of a novel tracked robot system that can be used for underwater fine surface cleaning operations. The robot uses a crawling track chassis and is equipped with a self-designed high-precision operating robotic arm, force-controlled grippers, and underwater brushing tools. Considering the complexity of the underwater environment, an underwater force-position hybrid control algorithm suitable for the robot is proposed, taking into account the water flow resistance. Its effectiveness has been verified in the experimental pool and the proposed robot system has been applied in the actual engineering site. The robot achieves a brushing efficiency of approximately 72 m$^{2}$ per hour, with a force control accuracy of $\pm$ 0.5 N, and the cleaning effect shows a significant improvement when compared to the condition before cleaning.

Globally, the prevalence of stroke is significant and increasing annually. This growth has led to a demand for rehabilitation services that far exceeds the supply, leaving many stroke survivors without adequate rehabilitative care. In response to this challenge, this study introduces a portable exoskeleton system that integrates neural control mechanisms governing human arm movements. This design leverages neuroplasticity principles to simulate natural movements, aiming to reactivate and strengthen neuromuscular connections and thus enhance rehabilitation outcomes. A tailored musculoskeletal model of the human arm and an associated cost function were developed to accurately replicate the planar motion trajectories of a healthy human arm across 32 directions. The application of a Proportional-Derivative (PD) controller enables precise tracking of these trajectories by the exoskeleton. Individual testing has demonstrated high consistency between the exoskeleton-driven motion paths and the simulated trajectories, especially in trajectory accuracy along the X and Y axes. These findings support the efficacy of integrating advanced neural control strategies with practical exoskeleton designs in stroke rehabilitation.

An aerial manipulator (AM) system for thickness measurement of metal facilities is introduced in this paper, which includes a fully actuated flying platform and an end-effector. The AM utilizes the fully actuated advantage of the flying platform to apply controlled contact forces to the environment without the need for complex robotic manipulators. An end-effector is designed, which includes an ultrasonic thickness (UT) probe mounted on a spring-damping buffer, and a coupling agent injection device allows the couplant to be applied during the inspection process. We divide the aerial inspection process into two phases: the approach phase and the contact phase. In the approach phase, we design a yaw control assistance method to ensure that the AM can contact the target surface with the desired yaw, even outside the operator’s line of sight. In the contact phase, a parallel force/velocity controller tailored to the motion characteristic of the AM is proposed to achieve precise contact force control. A switching function is designed to ensure stable transitions between motion control and force control. The control effectiveness and the ability to measure the thickness of the metal facility of the AM system are demonstrated by practical experiments.