Search Results (37)

Insulators on power lines require regular maintenance by operators in high-altitude hazardous environments, and the emergence of aerial manipulators provides an efficient and safe support for this scenario. In this study, a lightweight telescopic aerial manipulator system is developed, which can realize the installation and retrieval of insulator inspection robots on power lines. The aerial manipulator has three degrees of freedom, including two telescopic scissor mechanisms and one pitch rotation mechanism. Multiple types of cameras and sensors are specifically configured in the structure, and the total mass of the structure is 2.2 kg. Next, the kinematic model, dynamic model, and instantaneous contact force model of the designed aerial manipulator are derived. Then, the hybrid position/force control strategy of the aerial manipulator and the visual detection and estimation algorithm are designed to complete the operation or complete the task. Finally, the lifting external load test, grasp and installation operation test, as well as outdoor flight operation test are carried out. The test results not only quantitatively evaluate the effectiveness of the structural design and control design of the system but also verify that the aerial manipulator can complete the accurate automatic grasp and installation operation of the 3.6 kg target device in outdoor flight. Full article

Adopting mobile robots for high voltage (HV) live-line operations can mitigate personnel casualties and enhance operational efficiency. However, conventional mechanical arms cannot inspect concealed spaces in the power cable tunnel because their joint integrates metallic motors or hydraulic serial-drive mechanisms, which limit the arm’s length and insulation performance. Therefore, this study proposes a 7-degree-of-freedom (7-DOF) bionic mechanical arm with rigid-flexible coupling, mimicking human arm joints (shoulder, elbow, and wrist) designed for HV live-line operations in concealed cable tunnels. The arm employs a tendon-driven mechanism to remotely actuate joints, analogous to human musculoskeletal dynamics, thereby physically isolating conductive components (e.g., motors) from the mechanical arm. The arm’s structure utilizes dielectric materials and insulation-optimized geometries to reduce peak electric field intensity and increase creepage distance, achieving intrinsic self-insulation. Furthermore, the mechanical design addresses challenges posed by concealed spaces (e.g., shield tunnels and multi-circuit cable layouts) through the analysis of joint kinematics, drive mechanisms, and dielectric performance. The workspace of the proposed arm is an oblate ellipsoid with minor and major axes measuring 1.25 m and 1.65 m, respectively, covering the concealed space in the cable tunnel, while the arm’s quality is 4.7 kg. The maximum electric field intensity is 74.3 kV/m under 220 kV operating voltage. The field value is less than the air breakdown threshold. The proposed mechanical arm design significantly improves spatial adaptability, operational efficiency, and reliability in HV live-line inspection, offering theoretical and practical advancements for intelligent maintenance in cable tunnel environments. Full article

In power inspection fields, snake-like robots are often used for transmission line inspection tasks, requiring accurate identification of various power fittings. However, traditional visual sensors perform poorly under varying light intensity and complex background conditions. This paper proposes a non-visual perception method for the high-precision classification of different power fittings (e.g., vibration dampers, suspension clamps, and tension clamps) in snake-like robot transmission line inspection for high-voltage lines. This method, unaffected by light intensity changes, uses machine learning to classify the magnetic induction electromotive force signals around the fittings. First, the Dodd–Deeds eddy current model is used to analyse the magnetic field changes around the transmission line fittings and determine the induction coil distribution. Then, the concept of condition number and singular value decomposition (SVD) are introduced to analyse the impact of detection position on classification accuracy, with optimal detection positions found using the particle swarm optimization algorithm. Finally, a BP neural network optimised by a genetic algorithm is used for power fitting identification. Experiments show that this method successfully identifies vibration dampers, tension clamps, suspension clamps, and transmission lines at detection distances of 5 cm, 10 cm, 15 cm, and 20 cm, with accuracies of 99.8%, 97.5%, 95.1%, and 92.5%, respectively. Full article

Regular inspection of overhead power line (OPL) systems is required to detect damage early and ensure the efficient and uninterrupted transmission of high-voltage electric power. In the past, these checks were conducted utilizing line crawling, inspection robots, and a helicopter. Yet, these traditional solutions are slow, costly, and hazardous. Advancements in drones, edge computing platforms, deep learning, and high-resolution cameras may enable real-time OPL inspections using drones. Some research has been conducted on OPL inspection with autonomous drones. However, it is essential to explore how to achieve real-time OPL component detection effectively and efficiently. In this paper, we report our research on OPL component detection on edge computing devices. The original OPL dataset is generated in this study. In this paper, we evaluate the detection performance with several sizes of training datasets. We also implement simple data augmentation to extend the size of datasets. The performance of the YOLOv7 model is also evaluated on several edge computing platforms, such as Raspberry Pi 4B, Jetson Nano, and Jetson Orin Nano. The model quantization method is used to improve the real-time performance of the detection model. The simulation results show that the proposed YOLOv7 model can achieve mean average precision (mAP) over 90%. While the hardware evaluation shows the real-time detection performance can be achieved in several circumstances. Full article

To enhance the safety of hybrid inspection robots (HIRs) landing on power transmission lines (PTLs) with inclination and flexibility, this research derives a coupled dynamic model for a developed flying–walking power transmission line inspection robot (FPTLIR) to analyze the dynamic behavior of the FPTLIR during the landing process. The model uses the absolute nodal coordinate formulation (ANCF) for the dynamics of the PTL and the Hunt–Crossley theory for the contact model, integrating these components with the Euler–Lagrange method. A modular simulation was conducted to evaluate the effects of different landing positions and robot masses. An experimental platform was designed to evaluate the landing performance and validate the model, which confirms the method’s accuracy, with a mean relative Z-displacement error of 0.004. Simulation results indicate that Z-displacement decreases with increased landing distance, with the farthest point showing only 34.4% of the Z-displacement observed at the closest point. Conversely, roll increases, with the closest point exhibiting 3.7% of the roll at the farthest point. Both Z-displacement and roll are directly correlated with the robot’s mass; the lightest robot’s Z-displacement and roll are 9.2% and 12.8% of those of the heaviest robot, highlighting the safety advantage of lighter robots. This research enables precise analysis and prediction of the system’s responses during the landing process, providing significant insights for safe landing and design. Full article

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption. Full article

This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing real-time input and output information, the system’s pseudo-partial derivatives are assessed online. Leveraging these assessment parameters, a weighted one-step prediction control mechanism is designed, and a compact-form dynamic linearization model-free adaptive control framework is established. Moreover, the research incorporates compression mapping to thoroughly confirm the convergence of the algorithm, thereby ensuring its stability. Ultimately, the effectiveness and practicality of this control method are substantiated through a series of simulation experiments, demonstrating its robust performance. Full article

Industrial inspection is a well-known application area for unmanned aerial vehicles (UAVs), but are modern market-available drones fully suitable for inspections of larger-scale industrial facilities? This review summarizes the pros and cons of aerial large-scale facility inspection, distinguishing it from other inspection scenarios implemented with drones. Moreover, based on paper analysis and additionally performed experimental studies, it reveals specific issues related to modern commercial drone software and demonstrates that market-available UAVs (including DJI and Autel Robotics) more or less suffer from the same problems. The discovered issues include a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) shift, an identification of multiple images captured from the same point, limitations of custom mission generation with external tools and mission length, an incorrect flight time prediction, an unpredictable time of reaching a waypoint with a small radius, deviation from the pre-planned route line between two waypoints, a high pitch angle during acceleration/deceleration, an automatic landing cancellation in a strong wind, and flight monitoring issues related to ground station software. Finally, on the basis of the paper review, we propose solutions to these issues, which helped us overcome them during the first autonomous inspection of a 2400 megawatts thermal power plant. Full article

Power transmission lines frequently face threats from lightning strikes, severe storms, and chemical corrosion, which can lead to damage in steel–aluminum-stranded wires, thereby seriously affecting the stability of the power system. Currently, manual inspections are relatively inefficient and high risk, while drone inspections are often limited by complex environments and obstacles. Existing detection algorithms still face difficulties in identifying broken strands. To address these issues, this paper proposes a new method called SL-YOLOv8. This method incorporates an improved You Only Look Once version 8 (YOLOv8) algorithm, specifically designed for online intelligent inspection robots to detect broken strands in transmission lines. Transmission lines are susceptible to lightning strikes, storms, and chemical corrosion, which is leading to the potential failure of steel- and aluminum-stranded lines, and significantly impacting the stability of the power system. Currently, manual inspections come with relatively low efficiency and high risk, and Unmanned Aerial Vehicle (UAV) inspections are hindered by complex situations and obstacles, with current algorithms making it difficult to detect the broken strand lines. This paper proposes SL-YOLOv8, which is a broken transmission line strand detection method for an online intelligent inspection robot combined with an improved You Only Look Once version 8 (YOLOv8). By incorporating the Squeeze-and-Excitation Network version 2 (SENet_v2) into the feature fusion network, the method effectively enhances adaptive feature representation by focusing on and amplifying key information, thereby improving the network’s capability to detect small objects. Additionally, the introduction of the LSKblockAttention module, which combines Large Selective Kernels (LSKs) and the attention mechanism, allows the model to dynamically select and enhance critical features, significantly enhancing detection accuracy and robustness while maintaining model precision. Compared with the original YOLOv8 algorithm, SL-YOLOv8 demonstrates improved precision recognition accuracy in Break-ID-1632 and cable damage datasets. The precision is increased by 3.9% and 2.7%, and the recall is increased by 12.2% and 2.3%, respectively, for the two datasets. The mean average precision (mAP) at the Intersection over Union (IoU) threshold of 0.5 is also increased by 4.9% and 1.2%, showing the SL-YOLOv8’s effectiveness in accurately identifying small objects in complex situations. Full article

In the power grid, inspection robots enhance operational efficiency and safety by inspecting power lines for information sharing and interaction. Edge computing improves computational efficiency by positioning resources close to the data source, supporting real-time fault detection and line monitoring. However, large data volumes and high latency pose challenges. Existing offloading strategies often neglect task divisibility and priority, resulting in low efficiency and poor system performance. This paper constructs a power grid inspection offloading scenario using Python 3.11.2 to study and improve various offloading strategies. Implementing a game-theory-based distributed computation offloading strategy, simulation analysis reveals issues with high latency and low resource utilization. To address these, an improved game-theory-based strategy is proposed, optimizing task allocation and priority settings. By integrating local and edge computing resources, resource utilization is enhanced, and latency is significantly reduced. Simulations show that the improved strategy lowers communication latency, enhances system performance, and increases resource utilization in the power grid inspection context, offering valuable insights for related research. Full article

One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots’ camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting. Full article

For semiconductor manufacturing, automatic optical inspections (AOIs) are important for chip quality inspection. An AOI system contains a robot arm, an industrial camera, a x-y platform, and a visual inspection module. Using the industrial camera, a wafer map can be obtained and then sent to the visual inspection module to compare with qualified chip features. There is a baseline in the x-y platform. Due to the limitations of the robot arm flexibility, it is difficult for the robot arm to control the angles between the chip orientation and the baseline every time, which decreases the defect recognition accuracy. This work aims to improve the defect recognition accuracy and efficiency of the AOI system. Specifically, an efficient method is presented to calculate the angle between the baseline and chip orientation. Then, the wafer map can be rotated, such that the angle equals to zero. Further, a powerful system is established to recode the rotated chip coordinate, such that the unqualified chip positions can be located efficiently. This method is called a central array method. The central array method with deep learning methods forms an AI-based AOI system. Extensive experiments demonstrate that our proposed method performs well in improving the chip quality inspection efficiency and accuracy. Nevertheless, the proposed method still has challenges in implementation since it requires integration with the manufacturing line. Full article

In order to improve the flight efficiency of a flying–walking power transmission line inspection robot (FPTLIR) during flight missions, an accurate energy consumption model is constructed, and a multiobjective optimization approach using the improved NSGA-II is proposed to address the high energy consumption and long execution time. The energy consumption model is derived from the FPTLIR kinematics to the motor dynamics, with the key parameters validated using a test platform. A multiobjective optimization model is proposed that considers many constraints related to the FPTLIR during missions, offering a comprehensive analysis of the energy consumption and execution time. The NSGA-II algorithm is improved by integrating the Cauchy variation operator and the simulated annealing algorithm, which is used to construct the multiobjective optimization approach. Simulation and experimental results demonstrate that the proposed model accurately predicts the energy consumption of the FPTLIR across different paths and flight conditions with an average relative error ranging from 0.76% to 3.24%. After optimization, energy savings of 5.33% and 5.01% are achieved for on-line and off-line missions, respectively, while maintaining the shortest execution time at the given energy level. The energy consumption optimization approach significantly improves the flight efficiency of the system, providing a reference for analyzing and optimizing energy consumption of inspection robots. Full article

Operations and maintenance (O&M) of floating offshore wind turbines (FOWTs) require regular inspection activities to predict, detect, and troubleshoot faults at high altitudes and in harsh environments such as strong winds, waves, and tides. Their costs typically account for more than 30% of the lifetime cost due to high labor costs and long downtime. Different inspection methods, including manual inspection, permanent sensors, climbing robots, remotely operated vehicles (ROVs), and unmanned aerial vehicles (UAVs), can be employed to fulfill O&M missions. The UAVs, as an enabling technology, can deal with time and space constraints easily and complete tasks in a cost-effective and efficient manner, which have been widely used in different industries in recent years. This study provides valuable insights into the existing applications of UAVs in FOWT inspection, highlighting their potential to reduce the inspection cost and thereby reduce the cost of energy production. The article introduces the rationale for applying UAVs to FOWT inspection and examines the current technical status, research gaps, and future directions in this field by conducting a comprehensive literature review over the past 10 years. This paper will also include a review of UAVs’ applications in other infrastructure inspections, such as onshore wind turbines, bridges, power lines, solar power plants, and offshore oil and gas fields, since FOWTs are still in the early stages of development. Finally, the trends of UAV technology and its application in FOWTs inspection are discussed, leading to our future research direction. Full article

To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying–walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an enhanced method based on the Neural Radiance Fields (NeRF) method for reconstructing PTLs. The highlights of PL-NeRF include (1) compressing the unbounded scene of PTLs by exploiting the spatial compression of normal $L^{\infty }$; (2) encoding the direction and position of the sample points through Integrated Position Encoding (IPE) and Hash Encoding (HE), respectively. Compared to existing methods, the proposed method demonstrates good performance in 3D reconstruction, with fidelity indicators of PSNR = 29, SSIM = 0.871, and LPIPS = 0.087. Experimental results highlight that the combination of PL-NeRF with progressive motion sequence images ensures the integrity and continuity of PTLs, improving the efficiency and accuracy of image-based reconstructions. In the future, this method could be widely applied for efficient and accurate 3D reconstruction and inspection of PTLs, providing a strong foundation for automated monitoring of transmission corridors and digital power engineering. Full article