Inspection Path Research Articles

Human-machine collaborative inspection of transmission corridors based on the BeiDou positioning

Abstract Inadequate inspection performance of transmission lines leads to damages, posing threats to the safety of power grids. To enhance the efficiency of transmission line inspection and to reduce safety hazards, a method based on the BeiDou positioning for human-machine collaboration in transmission line inspection is proposed. Drones equipped with then BeiDou positioning are utilized to collect transmission line data via the Web Mercator projection, determining the inspection routes. Fault parameters of transmission lines are extracted for manual monitoring, identifying overlay current and voltage frequency fault characteristics. The optimal inspection route is then determined based on these features, achieving human-machine collaboration in transmission line inspection. Experimental results demonstrate that the proposed method detected 11 fault nodes, with inspection paths closely matching the transmission line structure. It identified six defects in power system transmission lines, with an inspection time of only 2.3 seconds. The area under the ROC curve, encompassing true positive and false positive rates, is approximately 5000, indicating the method’s effectiveness, efficiency, and accuracy in inspection.

Multi-UAV Path Planning for Inspection of Target Points with Stable Monitoring Frequencies

In this study, we focus on the path-planning problem of unmanned aerial vehicles (UAVs) deployed for inspection missions at target points. The goal is to visit each target point, provide revisits to important target points, and ultimately meet the monitoring requirements with regular and stable monitoring frequencies. Herein, we present MTSP-R, a novel variant of the multiple traveling salesmen problem (MTSP), in which revisits to important target points are allowed. We address the path-planning problem of multi-UAV in two stages. First, we propose a nearest insertion algorithm with revisits (NIA-R) to determine the number of required UAVs and initial inspection paths. We then propose an improved genetic algorithm (IGA) with two-part chromosome encoding to further optimize the inspection paths of the UAVs. The simulation results demonstrate that the IGA can effectively overcome the shortcomings of the original genetic algorithm, providing shorter paths for multiple UAVs and more stable monitoring frequencies for the target points.

Optimized structural inspection path planning for automated unmanned aerial systems

Automation in Unmanned Aerial Systems (UAS)-based structural inspections has gained significant traction given the scale and complexity of infrastructure. A core problem in UAS-based inspection is electing an optimal flight path to achieve the mission objectives while minimizing flight time. This paper presents an effective two-stage method that guarantees coverage as a constraint to ensure damage detectability, while minimizing path length as an objective. A genetic algorithm first determines viewpoint positions, and a greedy algorithm calculates the camera poses, as opposed to directly optimizing all degrees of freedom (DOF) simultaneously. A sensitivity analysis demonstrates the range of applicability and superiority of this formulation over direct 5-DOF optimization by at least 30 % shorter path length. Applied examples, including focused and partial space inspections, are also presented, demonstrating the flexibility of the proposed method to meet real-world requirements. The results highlight the feasibility of the approach and contribute to incorporating automation into UAS-based structural inspections.

Headspace-SERS assay for early mildewing tobacco leaves

Mildewed tobacco leaves seriously impact on cigarette product quality and pose a health risk to person. However, early moldy tobacco leaves are hardly found by naked eyes in the workshop. In this work, we self-assemble AuAg nanoalloys on silicon wafers to construct Si/AuAg chips. The headspace-surface enhanced Raman scattering (SERS) protocol is developed to monitor volatile 1,2-dichloro-3-methoxybenzene (2,3-DCA) and 2,4,6-trichloroanisole (2,4,6-TCA) released from postharvest tobacco. Consequently, the visualization of the SERS peak at 1592 cm−1 assigned to ν(CC) after headspace collection for 10 min and the SERS intensity ratio of 1054 and 1035 cm−1 from 2,3-DCA and 2,4,6-TCA less than 0.5 could be used as indicators to predict early moldy tobacco. Additionally, with headspace collection time prolonging to 2 h, a SERS band at 682 cm−1 due to ν(CCl) of 2,4,6-TCA occurs, confirming the mildew of leaves. The headspace-SERS protocol paves a path for rapid and on-site inspection of the quality of tobacco leaves and cigarettes during storage with a portable Raman system.

Drone inspection path planning with self-organizing neural network of elastic Hebbian learning rule

Abstract As the scale of the power grid continues to expand, inspection scenarios become increasingly complex, and inspection and maintenance work in the power industry faces huge challenges. The traditional manual inspection mode has problems such as high cost, low efficiency, and poor safety. Using electric drones to inspect all equipment nodes in the area can effectively reduce operation and maintenance costs and ensure personnel safety. As a key technology to realize autonomous power inspection, UAV path planning needs to overcome difficulties such as long solution time and intersection of inspection paths. Therefore, this article conducts research on UAV path planning algorithms for large-scale power inspections, which is of great significance to improving the efficiency of power inspections. To avoid the occurrence of path intersections and improve the path planning effect, this paper uses the convex hull property in graph theory to design elastic Hebbian learning rules and proposes a self-organizing neural network algorithm based on elastic Hebbian learning rules.

Automated Characterization of Macroscale Tissue 3D Spatial Material Properties via Robotically-Directed Impedimetric Sensing

Here, we report a novel method for automated characterization of bulk tissue 3D spatial properties based on reverse engineering-driven non-planar tool path planning and robotically-directed sensing. The method incorporates information on object (e.g., tissue) and inspection tool (e.g., sensor) geometry for automated inspection of tissue mechanical and dielectric properties across macroscopic nonplanar domains as large as 44 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{2}$</tex-math> </inline-formula> . The process avoids the need for manual sensor-tissue integration processes. The impact and the utility of the method were demonstrated by automated mapping of 3D spatial distributions of mechanical and dielectric properties of plant and animal tissues using multiple complementary impedimetric-based sensors of varying types and form factor, including rigid micro-electromechanical systems (MEMS) and flexible multi-functional fibers. Applications to automated characterization of food quality (e.g., type and age) are provided, including 3D spatial mapping of plant and animal tissue mechanical and dielectric property distributions. Ultimately, automated methods for 3D spatial inspection of plant and animal tissue properties are critical to agriculture, food processing, organ transplantation, and biomanufacturing industries. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the need for automating the inspection of soft 3D biological objects. Here, we present a method that generates 3D quality maps of tissue properties using several sensors. The proposed tool path planning program outputs a customized tissue-conforming path for inspection based on the topographical features of the tissue. Hence, this method enables high-throughput, spatially-resolved, minimally-invasive, and reliable inspection of soft 3D biological objects. Applications to inspection of food quality were provided using two impedimetric-based sensors. Practitioners can directly apply the framework to inspection of other tissue properties and soft objects. This work provides an advance in automated methods for inspection, and real-time monitoring of tissue 3D property distributions, which reduces the need for manual tissue handling and requirement for sensor-product interface prior to characterization. This work can be implemented in various biomanufacturing applications and industries, including food safety and quality control, tissue engineering, and organ transplantation, to ensure the quality and safety of macroscopic tissue-engineered medical and food products.

Optimization of offshore wind farm inspection paths based on K-means-GA.

As global demand for offshore wind energy continues to rise, the imperative to enhance the profitability of wind power projects and reduce their operational costs becomes increasingly urgent. This study proposes an innovative approach to optimize the inspection routes of offshore wind farms, which integrates the K-means clustering algorithm and genetic algorithm (GA). In this paper, the inspection route planning problem is formulated as a multiple traveling salesman problem (mTSP), and the advantages of the K-means clustering algorithm in distance similarity are utilized to effectively group the positions of wind turbines, thereby optimizing the inspection schedule for vessels. Subsequently, by harnessing the powerful optimization capability and robustness of genetic algorithms, further refinement is conducted to search for the optimal inspection routes, aiming to achieve cost reduction objectives. The results of simulation experiments demonstrate the effectiveness of this integrated approach. Compared to traditional genetic algorithms, the inspection route length has been significantly reduced, from 93 kilometers to 79.36 kilometers. Simultaneously, operational costs have also experienced a notable decrease, dropping from 141,500 Chinese Yuan to 125,600 Chinese Yuan.

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection

AbstractMotivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle multi‐source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real‐world scenarios. Afterward, a novel policy network named the attention‐based deep neural network (A‐DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A‐DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi‐head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi‐head attention model (DAM) has been tested in simulations and a real‐world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high‐quality path plan and show better generalizability for larger scale problem sizes compared to single‐head attention model (SAM), multi‐head attention model (AM), and two baseline models, namely OR‐Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision‐making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments.

Geometric error compensation method using the Laser R-test

Traditional methods for measuring geometric errors in machine tools, including interferometry and Double Ball Bar (DBB), are known to be expensive and time-intensive. Consequently, a non-contact calibration system called the “Laser R-test” has been developed. This innovative system is designed to measure both position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs) efficiently. Since its development in 2000, this tool has been instrumental in analyzing eccentricity errors, angular position errors, and simultaneous trajectory errors. Through extensive research, it has been determined that the total error in a five-axis machine tool can be controlled to below 40 µm after compensating for eccentricity parameters and angular position errors. However, reducing this error to below ± 10 µm is challenging, primarily due to wobble errors in the orientation of the rotary axis without compensating. In this study, a new methodology based on Laser R-test and Rodrigues’ rotation formula has been developed to establish a PIGE model of rotary axis. Based on the methodology, the 8 PIGEs can be analyzed by measuring 5 coordinate positions. The compensation of 8 PIGEs in the rotary axis is completed within 30 min using the inspection path. Compatibility with ISO-10791–6 standards for BK1, BK2, and BK4 path tests is confirmed, validating the compensation effects. A precision of below ± 10 µm is achieved, with inspection time reduced by over 50%. This system can complete multiple errors by simply using the different paths. This greatly reduces the setup time for future users, enhancing its commercial applicability.

A Methodology for Ship Cabin Equipment Layout Considering Human Factor Reliability Optimization

_ The paper proposes an innovative methodology of Human Cognitive Reliability- Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A* search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A* search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP. Keywords ship cabin equipment layout; human factor reliability optimization; HCRCREAM; A* search; genetic algorithm

Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection.

Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA's search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping-firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms.

Application of Optimization Algorithms for Optimal Inspection Path Planning in Multi-point Distribution Stations

Application of Optimization Algorithms for Optimal Inspection Path Planning in Multi-point Distribution Stations

A rapid unmanned aerial vehicle inspection path planning method based on hybrid heuristic algorithm

The advancement of unmanned aerial vehicle (UAV) and remote sensing technologies has fueled interest in automatic UAV inspection path planning based on inspection tasks. However, traditional methods suffer from limitations such as manual operation, inability to find optimized paths, and lengthy time consumption. We propose a rapid UAV inspection path planning method using a hybrid heuristic algorithm in this paper. First, real-world data is abstracted into a graph. Then, a hybrid heuristic algorithm is proposed and used to determine an optimal inspection path considering the tasks and power parameters of the UAV. Finally, the total power consumption for the mission is computed to assess UAV battery capacity adequacy. Experimental results demonstrate the effectiveness of our method in terms of both performance and accuracy.

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

Multiaxis On-Machine Inspection Path Planning for Free-Form Impeller Based on Least Rotation Times Optimization Model

Multiaxis On-Machine Inspection Path Planning for Free-Form Impeller Based on Least Rotation Times Optimization Model

CAD-based Autonomous Vision Inspection Systems

Automated industrial Visual Inspection Systems (VIS) are typically customized for specific applications, limiting their flexibility. They are characterized by a demanding setup, high capital investments, and significant knowledge barriers. In this paper, we propose an alternative architecture for the visual inspection of 3D printed parts or complex assemblies using a robotic arm equipped with hand-eye sensors and controllable lighting system. The core of the proposed Flexible Vision Inspection System (FVIS) is the self-extraction of 3D text annotations from STandard for the Exchange of Product model (STEP) AP242 files. The system self-selects and parametrizes the most suitable inspection algorithm, including lighting settings. Additionally, it autonomously performs self-localization, self-referencing of physical products, and self-planning of robot inspection path based on CAD information. This framework, characterized by self-X, cost-effective, non-invasive, and plug-and-play architecture has the potential to disrupt the business model of vision inspection, enabling an as-a-service solution aligned with the next generation of flexible manufacturing.

Task-Driven Path Planning for Unmanned Aerial Vehicle-Based Bridge Inspection in Wind Fields

Using unmanned aerial vehicles (UAVs) for bridge inspection is becoming increasingly popular due to its ability to improve efficiency and ensure the safety of monitoring personnel. Compared to traditional manual monitoring methods, UAV inspections are a safer and more efficient alternative. This paper examines the impact of meteorological conditions on UAV-based bridge monitoring during specific tasks, with the aim of enhancing the safety of the UAV’s costly components. The wake vortex behind a bridge structure can vary over time due to airflow, which can have a direct impact on the safety of UAV flights. To assess this impact, numerical analysis is conducted based on monitoring requirements specific to different tasks, taking into account wind speed, wind direction, and air temperature. In order to optimize UAV trajectory, it is important to consider the wake vortex intensity and its associated influence region, which can pose a potential danger to UAV flight. Additionally, the analysis should take into account the aerodynamic effects of different types of bridge columns on the wake vortex. An optimization algorithm was utilized to optimize the trajectory of a UAV during bridge inspections within the safe region affected by wind fields. This resulted in the determination of an effective and safe flight path. The study reveals that varying wind speeds have an impact on the safe flight zone of UAVs, even if they are below the operational requirements. Therefore, when monitoring bridges using UAVs, it is important to take into account the influence of meteorological conditions. Furthermore, it was observed that the flight path of UAVs during square cylinder column monitoring is longer and more time-consuming than round cylinder column monitoring. Determining an effective UAV inspection path is crucial for completing bridge monitoring tasks in windy conditions, establishing bridge inspection standards, and developing the Intelligent Bridge Inspection System (IBIS).

Opportunities, Paths and Guarantees for Practitioners to Participate in Discipline Construction — Take Discipline Inspection and Supervision as an Example

Under the background of the initial establishment of discipline inspection and supervision, this study discusses the opportunity, path and guarantee for discipline inspection and supervision practitioners to participate in discipline construction based on the practicality of discipline inspection and supervision itself. It expounds the most effective way of discipline inspection and supervision is to construct a new pattern of discipline construction in which practitioners of discipline inspection and supervision and professional researchers cooperate and complement each other. Taking the cooperation between discipline inspection and supervision practitioners and professional researchers as the starting point, it puts forward the possible path for discipline inspection and supervision practitioners to participate in the discipline construction. At the same time, in order to fully ensure that the discipline inspection and supervision practitioners effectively participate in the construction of discipline inspection and supervision, and better realize their own career development, the discipline inspection and supervision organs (institutions) proposed to improve the institutional guarantee based on party discipline and regulations, make good use of the existing platform to open up communication channels, and increase support by relying on scientific research projects.

Cooperative Path Planning of Ground-air Robots for Distributed Photovoltaic Inspection

Facing the Distributed PV generation inspection scenario, to overcome the low efficiency of traditional manual inspection and the insufficient endurance of existing UAV inspection, UAV aerial inspection and UGVs to provide energy supply in the road network are adopted, and a path planning method that prioritizes the meeting points is proposed. Firstly, the improved genetic algorithm is used to plan the inspection path of each UAV, then the UAV path is clustered to determine the optimal number of meeting points, then the adaptive particle swarm algorithm is used to find the best location of the meeting points in the road network, and finally, the patrol path with the meeting points is optimized twice. Through the simulation test of the standard data set and real data, the effectiveness of the proposed method in solving the path planning problem of distributed photovoltaic coordinated inspection by ground-to-air robots is verified.