Inspection Path Research Articles

A sequential Stackelberg game for dynamic inspection problems

We introduce an inspection game where one inspector has the role of monitoring a group of inspectees. The inspector has the resources to visit only a few of them. Visits are performed sequentially with no repetitions. The inspectees report and share the sequence of inspections as they occur, but otherwise, they do not cooperate. We formulate two Stackelberg models, a static game where the inspector commits to play a sequence of visits announced at the start of the game, and a dynamic game where visits will depend on who was visited previously. In the static game, we characterize the (randomized) inspection paths in an equilibrium using linear programs. In the dynamic game, we determine the inspection paths in an equilibrium using backward induction.Our paper focuses on the mathematical structure of the equilibria of this sequential inspection game, where the inspector can perform exactly two visits. In the static game, the inspection paths are solutions to a transportation problem. We use this equivalence to determine an explicit solution to the game and to show that set of inspection path probabilities in an equilibrium, projected onto its first and second visit marginals, is convex. We discuss how the static and dynamic games relate to each other and how to use these models in practical settings.

The multi-objective inspection path-planning in radioactive environment based on an improved ant colony optimization algorithm

More and more occupational workers work in radioactive environment. Although measures are taken to keep the radiation dose in a safe range, the workers will suffer more radiation during the overhauling of nuclear power plants. The dose they suffer during the overhauling of nuclear power plants account for 80% of the total annual dose so it is necessarily to plan a reasonable inspection path for them according to the safety principle of as low as reasonably achievable (ALARA). An improved ant colony optimization (IACO) algorithm is proposed to solve the multi-objective inspection path-planning problem in radioactive environment. To improve the performance of the algorithm, we not only combine ant colony optimization (ACO) algorithm and chaos optimization algorithm, but also introduce pheromone differentiated update strategy and local search optimization strategy. Additionally, 5 experimental simulation cases are conducted and the results are compared to those from particle swarm optimization (PSO) algorithm, chaos particle swarm optimization (CPSO) algorithm and tradition ACO algorithm. In the first three cases, the probability of IACO algorithm finding the optimal path is obviously greater than that of PSO algorithm and CPSO algorithm. IACO algorithm is more efficient and stable. By analyzing an inspection path-planning case with 35 target positions and a case with 44 target positions in a more complex radioactive environment, IACO algorithm could find the path with less effective dose that ACO algorithm cannot find. Therefore, the effectiveness and validity of IACO to solve multi-objective inspection path-planning problem in radioactive environment are verified by experimental simulations and it can help workers reduce radiation exposure.

Specifying and optimizing robotic motion for visual quality inspection

Installation or even just modification of robot-supported production and quality inspection is a tedious process that usually requires full-time human expert engagement. The resulting parameters, e.g. robot velocities specified by an expert, are often subjective and produce suboptimal results. In this paper, we propose a new approach for specifying visual inspection trajectories based on CAD models of workpieces to be inspected. The expert involvement is required only to select – in a CAD system – the desired points on the inspection path along which the robot should move the camera. The rest of the approach is fully automatic. From the selected path data, the system computes temporal parametrization of the path, which ensures smoothness of the resulting robot trajectory for visual inspection. We then apply a new learning method for the optimization of robot speed along the specified path. The proposed approach combines iterative learning control and reinforcement learning. It takes a numerical estimate of image quality as input and produces the fastest possible motion that does not result in the degradation of image quality as output. In our experiments, the algorithm achieved up to 53% cycle time reduction from an initial, manually specified motion, without degrading the image quality. We show experimentally that the proposed algorithm achieves better results compared to some other policy learning approaches. The described approach is general and can be used with different types of learning and feedback signals. • Robot-supported visual quality inspection requires accurate position and velocity tuning. • Human-determined velocities are suboptimal and might reduce the quality of the captured images. • A new approach for specifying spatial and temporal course of visual inspection trajectories based on CAD models of workpieces to be inspected. • A new learning method for the optimization of robot speed along the specified path. • Real-world results show a significant improvement of inspection times with no image quality degradation.

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

Robotic Coverage Path Planning for Ultrasonic Inspection

Automatic robotic inspection of arbitrary free-form shapes is relevant for many quality control applications in different industries. We propose a method for planning the motion of an industrial robot to perform ultrasonic inspection of varying 3D shapes. Our method starts with the calculation of a set of sub-paths. These sub-paths are derived from streamlines. The underlying vector field is deduced from local curvature of the inspected geometry. Intermediate robot motions are planned to connect individual sub-paths to obtain a single complete inspection path. Coverage is calculated via ray tracing to simulate the propagation of ultrasound signals. This simulation enables the algorithm to proceed adaptively and to find a good trade-off between path length and coverage. We report experiments for four different geometries. The results indicate that shorter paths are achieved by using ray tracing for adaptive adjustment of streamline density. Our algorithm is tailored to ultrasonic inspection. However, the main concept of exploiting local surface curvature and streamlines for coverage path planning generalizes to other robotic inspection problems.

Correlation between photoluminescence and electroluminescence in GaN-related micro light emitting diodes: Effects of leakage current, applied bias, incident light absorption and carrier escape

Accurately predicting the peak wavelength and intensity of the electroluminescence spectrum through the photoluminescence method which can be measured without connecting electrodes is not only practically important for the development of micro-LEDs that cannot be fully inspected for device development, but there is also a fundamental importance in understanding the light emitting mechanism of the LEDs. In this study, the correlation and detailed mechanism between PL and EL are systematically analyzed by considering the effects of carrier escape, carrier accumulation, applied bias, leakage current, incident light wavelength and light absorption in order to provide a clear path for all-optical inspection of light emitting diodes and complete understanding of the luminescence mechanism in GaN-related quantum wells. • To correlate the photoluminescence to the electroluminescence in GaN-related micro LEDs, detailed mechanism of PL and EL are systematically analyzed. • Direct experimental results on the sequential depletion process and on the wavefunction overlap in an individual quantum well were obtained. • In order to correlate the PL with EL, matching the concentration of carriers participating in recombination in both measurements is important. • Effect of carrier leakage plays an important role in EL in micro-LEDs which operates just above the turn on voltages. • Simultaneous excitation of 325 nm and 405 nm lasers can provide most similar carrier distribution as in EL by injecting carriers both in QWs and p-GaN.

Design of autonomous navigation system for electric patrol Unmanned Aerial Vehicle Based on Beidou Positioning and satellite based enhancement

In order to reduce the deviation of automatic inspection of transmission tower UAV and improve the efficiency of inspection work, the design of autonomous navigation system of electric power inspection UAV Based on Beidou Positioning and satellite based enhancement is proposed. On the basis of Beidou Positioning and satellite based enhancement, the automatic inspection projector and automatic inspection navigator of transmission tower UAV are designed, and the hardware design of the system is completed. This paper analyzes the endurance of UAV, calculates the coordinates of the best shooting point of UAV, and completes the planning of automatic inspection path of transmission tower UAV according to the horizontal offset coordinates of UAV in the process of transmission tower inspection. Combined with the automatic inspection algorithm of transmission tower UAV, the system software design is completed to realize the automatic inspection of transmission tower UAV. The simulation results show that compared with the traditional inspection system, the deviation of the designed UAV automatic inspection system for transmission tower is significantly reduced.

Probe Path Planning for Flatness Measurement on Coordinate Measuring Machine Using Ant Colony Optimization

Optimization of a flatness error inspection activity on coordinate measuring machine (CMM) is a very crucial problem which demands minimization of a probe path for productive inspection. In the present work, the approach is explained to minimize the total probe travelling length and hence, the time of flatness inspection. Three sampling methods with eight sample sizes have been considered for this work. The ant colony optimization (ACO) algorithm based on travelling salesman problem (TSP) approach was developed in MATLAB environment to find the shortest probe paths. It was verified that the probe path depends on the sampling method used to measure the flatness. The sampling method giving the shortest probe path was selected as the best-suited method for a particular sample size. The results obtained by analyzing an illustrative example shows that the proposed approach is both effective and optimum.

High accuracy on-machine inspection based on rotation theory of multi-axis machine tool

On-machine inspection is an important technology for the precision fabrication. Plagued by overlapping structure and positioning error from machine tool, it is a challenge to generate an on-machine inspection path. In this paper, a rotation theory is investigated for balancing efficiency and accuracy. Based on the kinematic chain of the machine tool, an accessible space graph is established to demonstrate this theory. The least rotations, the least direction reversions can be recognized from this graph. In term of these principles, an orientation planning method is proposed for the parts with overlapping free-form surface. At last, the advantages of proposed path generation method are verified on an impeller’s on-machine inspection.

A novel methodology for the path alignment of visual SLAM in indoor construction inspection

Path alignment is the process of mapping an indoor construction inspection path reconstructed by a visual SLAM system onto a 2D map with user interaction required to pinpoint at least two common tie points. In practice, more points are often needed due to path distortions and linear transformations, potentially resulting in reduced productivity. This paper proposes a methodology that combines two novel algorithms for the path alignment: (1) PCA_STAN_ALGO applies principal component analysis to remove path distortions caused by the xz plane of a camera coordinate system not being parallel to the floor plane; and (2) GRPX_TRANS utilizes a graphical user interface to facilitate the path alignment. The proposed methodology enables the users to utilize just two tie points for successful path alignment. An experimental study showed that applying both PCA_STAN_ALGO and GRPX_TRANS saved about 50% in time compared to using only GRPX_TRANS, a result of needing minimal moving points.

Path Planning for a Solar-Powered UAV Inspecting Mountain Sites for Safety and Rescue

This paper focuses on the application using a solar-powered unmanned aerial vehicle (UAV) to inspect mountain sites for the purpose of safety and rescue. An inspection path planning problem is formulated, which looks for the path for an UAV to visit a set of sites where people may appear while avoiding collisions with mountains and maintaining positive residual energy. A rapidly exploring random tree (RRT)-based planning method is proposed. This method firstly finds a feasible path that satisfies the residual energy requirement and then shortens the path if there is some abundant residual energy at the end. Computer simulations are conducted to demonstrate the performance of the proposed method.

Smart Coordinate Measuring Machine (CMM) Inspection for Turbine Guide Vanes with Trend Line and Geometric Profile Tolerance

Turbine guide vanes are among the most critical and complex turbine parts. As an entire engine comprises a significant number of vanes, simplification of the measurement process translates into overall time and money savings. The key to simplification is to define critical areas for inspection, which enables relaxation of strict inspection standards in all areas of stable process manufacturing. The method described herein can help engineers to achieve savings in inspection time and cost, at the same time ensuring the correct shape of vanes as the approach used in this work places great emphasis on correlations between measurements, working conditions, and manufacturing abilities. Another element of the novelty of this approach is an atypical hybrid convention for the crossing of vertical and horizontal inspection paths, assuring a correlation between the measured sections. Although this novel approach was used to measure the geometry of a cast turbine guide vane, it can be easily implemented to measure the geometry of any other element of complex shape.

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N‐P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

KERAGAMAN DAN KEMIRIPAN SPESIES BURUNG PADA TIGA TIPE HABITAT DI KAWASAN HUTAN DENGAN TUJUAN KHUSUS UNIVERSITAS LAMBUNG MANGKURAT (KHDTK ULM)

There are no publications on bird diversity in the Forest Area for Special Purpose, Lambung Mangkurat University (KHDTK ULM). This study aimed to analyze the diversity and similarity of bird species in the secondary natural forest, the plantation forest, and shrubs in the KHDTK ULM. Data related to diurnal birds was collected in these three habitat types. Researchers walked slowly following the transects (footpaths, inspection paths) that were already in each habitat type and recorded the name of the species and the number of individuals observed within 25 m between 06.30 - 10.00 and 15.00 - 18.00 in three replications. The data was processed to obtain diversity index of bird species (Shannon-Wienner index) and similarity index of species, as well as protection and conservation status according to LHK Regulation No. 106 of 2018 and its conservation according to IUCN. Twenty-eight species or 17 bird families were found in the area. Three of them are protected and most are categorized as least concern. The habitat types with species diversity index from highest to lowest are plantation forest, shrubs, and secondary natural forest. Based on the similarity index, the highest similarity of bird species occurs between secondary natural forest and shrub, while the lowest is between secondary natural forest and plantation forest.Keywords: diversity; forest are; habitat type; similarity; special purpose

Optical inspection path planning for large liquid crystal glass substrate detection instrument.

A large liquid crystal glass substrate detection instrument usually uses the air flotation platform to support a glass substrate. The glass substrate will be deformed by air pressure on the air flotation platform, which will affect the accuracy of detection. In this paper, combined with the characteristics of the detection method of the detection instrument, an optimal detection scanning path with the minimum deflection variation range of the glass substrate is planned so as to minimize the influence of glass substrate deformation on the detection accuracy. In theory, the calculation model of the deflection variation ranges of different paths on the glass substrate is established, the numerical method is used to solve it, and finally, the optimal scanning path for the optical inspection is obtained. The experimental results verify the correctness of the model.

UAV Inspection Path Planning Based on Transmission Line Technology

With the continuous progress of human society and the rapid development of economy, the power industry closely related to human production and life is also developing rapidly. Electric power industry is also playing an increasingly important role in the development of national economy. Subsequently, transmission security becomes more and more important. However, at present, the traditional single manual line inspection has the characteristics of high labor intensity, high safety risk, high safety risk, low operation efficiency, and many deficiencies seriously restrict the intelligent transformation of transmission line operation and maintenance. Therefore, this paper proposes a new transmission line detection scheme based on unmanned aerial vehicle (UAV) detection path planning. Transmission line detection system based on UAV and ground monitoring equipment basically meets the requirements of transmission line detection. It is found that the UAV based transmission line inspection work arrangement efficiency is higher, and the fault detection rate is higher. The accuracy rate of UAV line inspection method proposed in this paper reaches 93.7%, which can effectively reduce the workload of staff and improve the work efficiency.

Online coverage and inspection planning for 3D modeling

In this study, we address an exploration problem when constructing complete 3D models in an unknown environment using a Micro-Aerial Vehicle. Most previous exploration methods were based on the Next-Best-View (NBV) approaches, which iteratively determine the most informative view, that exposes the greatest unknown area from the current partial model. However, these approaches sometimes miss minor unreconstructed regions like holes or sparse surfaces (while these can be important features). Furthermore, because the NBV methods iterate the next-best path from a current partial view, they sometimes produce unnecessarily long trajectories by revisiting known regions. To address these problems, we propose a novel exploration algorithm that integrates coverage and inspection strategies. The suggested algorithm first computes a global plan to cover unexplored regions to complete the target model sequentially. It then plans local inspection paths that comprehensively scans local frontiers. This approach reduces the total exploration time and improves the completeness of the reconstructed models. We evaluate the proposed algorithm in comparison with other state-of-the-art approaches through simulated and real-world experiments. The results show that our algorithm outperforms the other approaches and in particular improves the completeness of surface coverage.

Optimal path planning for automated dimensional inspection of free-form surfaces

Structural dimensional inspection is vital for the process monitoring, quality control, and fault diagnosis in the mass production of auto bodies. Comparing with the non-contact measurement, the high-precision five-axis measuring machine with the touch-trigger probe is a preferred choice for data collection. It can assist manufacturers in making accurate inspection quickly. As the increase of free-form surfaces and diverse surface orientations in product design, existing inspection approaches cannot capture some new critical features in the curvature of products in an efficient way. Therefore, we need to develop new path planning methods for automated dimensional inspection of free-form surfaces. This paper proposes an optimal path planning system for automated programming of measuring point inspection by incorporating probe rotations and effective collision detection. Specifically, the methodological contributions include: (i) a dynamic searching volume-based algorithm is developed to detect potential collisions in the local path between measurement points; (ii) a local path generation method is proposed with the integration of the probe trajectory and the stylus rotation. Then, the inspection time matrix is proposed to quantify the measuring time of diverse local paths; (iii) an optimization approach of the global inspection path for all critical points on the product is developed to minimize the total inspection time. A case study has been conducted on an auto body to verify the performance of the proposed method. Results show that the collision-free path for the free-form auto body could be generated automatically with off-line programming, and the proposed method produces about 40% fewer dummy points and needs 32% less movement time in the auto body inspection process.

A co-optimal coverage path planning method for aerial scanning of complex structures

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm – but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to safety and inspection requirements. The result is a highly parallelizable algorithm that produces more efficient paths where quality of the useful image data is improved. The path optimization algorithm utilizes a particle swarm optimization (PSO) framework which iteratively optimizes the coverage paths without needing to discretize the motion space or simplify the sensing models as is done in similar methods. The core of the method consists of a cost function that measures both the quality and efficiency of a coverage inspection path, and a greedy heuristic for the optimization enhancement by aggressively exploring the viewpoints search spaces. To assess the proposed method, a coverage path quality evaluation method is also presented in this research, which can be utilized as the benchmark for assessing other CPP methods for structural inspection purpose. The effectiveness of the proposed method is demonstrated by comparing the quality and efficiency of the proposed approach with the state-of-art through both synthetic and real-world scenes. The experiments show that our method enables significant performance improvement in coverage inspection quality while preserving the path efficiency on different test geometries.

A spiral-based inspection path generation algorithm for efficient five-axis sweep scanning of freeform surfaces

Traditional inspection of freeform surfaces via three-axis or three ＋ two-axis CMM mainly works in a point-by-point manner where for each inspection point, the stylus of CMM has to approach to and then retract from the surface of inspection, which inevitably drags down (often substantially) the inspection efficiency. Recently, a new inspection apparatus called five-axis inspection machine has emerged, on which the stylus tip can continuously sweep on the part surface and the inspection efficiency can thus be tremendously improved. The crux in using this new inspection technology is how to plan an efficient five-axis inspection path (including both the position and orientation of the stylus). In this paper, we present a new type of five-axis inspection path generation strategy for the inspection of a general freeform surface. Based on the proposed index called Surface Sweep Rate (SSR), an algorithm is designed to partition the surface into a single spiral strip, which maximizes the overall SSR. Then, for this single spiral strip, a five-axis inspection path is generated that takes into consideration the key constraints such as the required smoothness of the probe head trajectory, the sampling density, and the collision-free condition. Physical inspection experiments of our proposed strategy are conducted on two typical freeform surfaces, and the results convincingly confirm the feasibility, effectiveness, and advantages of our new solution.