Multi‐sensors in‐line inspection robot for pipe flaws detection

Le, Dan; Chen, Zhiyuan; Rajkumar, R.

doi:10.1049/iet-smt.2019.0171

Cited by 12 publications

(6 citation statements)

References 48 publications

(45 reference statements)

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“…The authors classified five types of crack conditions, including good, satisfactory, fair, critical, and failure, to provide information about the severity. Le et al developed a mobile robotic system for the in-line inspection of the pipes [26]. The authors integrated multiple sensors (e.g., LIDAR, optic sensors) on the robot and classified these combined sensory data using the SVM algorithm for detecting cracks in pipes.…”

Section: Learning-based Methodsmentioning

confidence: 99%

“…The researchers collected centimeter-level spatial resolution images and utilized a hybrid model (ANN+SVM) to inspect the cracks. In [20,[25][26][27], machine learning techniques were introduced for crack assessment, improving upon the conventional methods. By using legged robots, mobile robots, and UAVs, these studies leveraged tactile sensory systems, the fusion of camera data with other sensors, and image analysis models such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).…”

Section: Learning-based Methodsmentioning

confidence: 99%

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Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

et al. 2023

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Crack inspection is important to monitor the structural health of pavement structures and make the repair process easier. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. To solve the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates the pavement and collects visual images from the surface. A deep-learning-based semantic segmentation framework named RCDNet was proposed. The RCDNet was implemented on the onboard computer of the robot to identify cracks from the visual images. The encoder-decoder architecture was utilized as the base framework of the proposed RCDNet. The RCDNet comprises a dual-channel encoder and a decoder module. The encoder and decoder parts contain a context-embedded channel attention (CECA) module and a global attention module (GAM), respectively. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested in both indoor and outdoor environments. The robot was observed to complete the inspection of a 3 m × 2 m grid within 10 min and a 2.5 m × 1 m grid within 6 min. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map was generated using the visual image results. This map highlights areas that require greater attention for repair in the test grid.

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Section: Learning-based Methodsmentioning

confidence: 99%

Section: Learning-based Methodsmentioning

confidence: 99%

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

et al. 2023

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“…Research on the operating system (QuigleyGerkey et al, 2009) to integrate different components on the robotic system, sensors fusions (Le et al, 2020;Gibb et al, 2018) to achieve a higher accuracy data acquisition process, and data transferring networks (Calvaresi and Calbimonte, 2020) for data management and processing…”

Section: Development Of Robot Componentsmentioning

confidence: 99%

A guide for construction practitioners to integrate robotic systems in their construction applications

Prieto,

Xu,

García de Soto

2024

Front. Built Environ.

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Introduction: The use of robots can boost productivity and reduce costs in construction projects. However, choosing the right robotic platform for the right application can be challenging, costly, and time-consuming. This paper presents a guide for researchers and construction practitioners interested in using robotic systems.Methods: A methodology covering the different aspects to be considered when it comes to a robotic platform, such as the framework (both hardware and software), the environment the robot is going to be operating, the level of supervision (i.e., autonomy) the robot requires, different hardware required on board (i.e., sensors and computers), and the control strategies and systems and communication network, is presented. The methodology is implemented with a practical application where a semi-autonomous robotic system is designed and developed with the simple goal of data collection on construction sites, making sure that all the steps covered in the methodology are addressed.Results: The results show that the methodology is applicable to a wide range of solutions, with a focus on the development of the platform itself and not the final application.Discussion: This guide is meant to assist in developing a flexible open platform that can be customized to the specific requirements needed.

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“…Embedded machine vision system benefits from its advantages of small system scale and high integration and is widely used in application fields with high research value [15]. e execution efficiency of vision algorithms to a certain extent restricts the efficiency of many current machine vision systems, resulting in some computationally intensive vision systems that do not have high execution efficiency [16]. Sun Q et al propose an anonymous aggregated encryption scheme that encrypts several different messages into a single ciphertext and sends them to multiple end users, each of whom decrypts the message using the decryption key they have to obtain the corresponding plaintext message, which effectively reduces the computational overhead in industrial IoT systems [17].…”

Section: Key Technologymentioning

confidence: 99%

Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism

Wang

Liu

2022

Mathematical Problems in Engineering

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This paper introduces the development status of machine vision nondestructive testing technology and industrial IoT supervision mechanism. The study designs and implements a machine vision nondestructive testing system from two aspects: construction of industrial IoT supervision and detection model, and optimization of machine vision nondestructive testing algorithm. In this paper, the random deployment of dynamic and static nodes is adopted. The coverage rate after random deployment and the moving distance of dynamic nodes are two necessary research parameters. To improve the initial coverage and optimize the mobile path of dynamic nodes, this paper proposes a mobile deployment optimization scheme based on the supervisory mechanism model of industrial IoT, which improves the traversal of the quantum genetic algorithm by improving the genetic variation rules, thus improving the initial deployment of the network. The optimized machine vision nondestructive detection algorithm is used for mobile path optimization from dynamic nodes to target locations. Simulation results show that a random deployment of 100 static nodes and 20 dynamic nodes in a 400 m × 400 m factory area works best with a coverage rate of 6.719% and an average movement distance of 23.47 m, and the movement path avoids the obstacle area. The average accuracy of the modified machine vision nondestructive testing system is 1.59% higher than that before the modification, and the average detection accuracy of the final experiment reaches 95.46%. Not only is the coverage rate better than that of the cellular structure-based dynamic node optimization scheme, but also the monitoring range of the plant tends to be more comprehensive in the actual deployment environment. Through the analysis of test results, the system achieves the monitoring and display of data on the one hand and provides a natural information access and interaction experience for IoT managers on the other hand, which meets the requirements of real time, accuracy, and stability of industrial IoT information data to a certain extent.

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Multi‐sensors in‐line inspection robot for pipe flaws detection

Cited by 12 publications

References 48 publications

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

A guide for construction practitioners to integrate robotic systems in their construction applications

Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism

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