Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments

Implementation of explosion safety regulations in design of a mobile robot for coal mines

The article focuses on specific challenges of the design of a reconnaissance mobile robotic system aimed for inspection in underground coal mine areas after a catastrophic event. Systems that are designated for these conditions must meet specific standards and regulations. In this paper is discussed primarily the main conception of meeting explosion safety regulations of European Union 2014/34/EU (also called ATEX-from French "Appareils destines a etre utilises en ATmospheres Explosives") for Group I (equipment intended for use in underground mines) and Category M1 (equipment designed for operation in the presence of an explosive atmosphere). An example of a practical solution is described on main subsystems of the mobile robot TeleRescuera teleoperated robot with autonomy functions, a sensory subsystem with multiple cameras, three-dimensional (3D) mapping and sensors for measurement of gas concentration, airflow, relative humidity, and temperatures. Explosion safety is ensured according to the Technical Report CLC/TR 60079-33 "s" by two main independent protections-mechanical protection (flameproof enclosure) and electrical protection (automatic methane detector that disconnects power when methane breaches the enclosure and gets inside the robot body).Web of Science811art. no. 230

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

Robot-aided tunnel inspection and maintenance system by vision and proximity sensor integration

This article describes an unprecedented alternative to manual procedures for the application of advanced composite materials, such as Fiber Reinforced Polymer (FRP) and epoxy resins. A complete mobile integrated system is presented for the inspection and maintenance of concrete surfaces in tunnels. It allows performance of operations with minimum interference on passing traffic. The core of this system resides in a specially designed light-weight robotic tool, which is sensed and automated for processes. Sensing includes vision and a laser telemeter to assure precise inspection, superficial preparation, and composite application. The designed interconnection flange allows simple and robust attachment of the tool to a robotic arm's tip. The robot&-tool set is to be mounted on a standard articulated lift platform. Therefore, an operator can direct the platform and the robot&-tool set's operations from a control station placed at ground-level, in a wheeled vehicle on which the articulated lift platform is mounted. A graphical Human&-Machine Interface (HMI) has been developed for the system. It allows the operator to identify fissures for the injection of epoxy resin, and weakened surfaces for FRP adhesion. Actual procedures are planned and performed by the system's automatic components.This work has been supported by the CAM Project S2009/DPI-1559/ROBOCITY2030 II, developed by the research team RoboticsLab at the University Carlos III of Madrid

Design and Development of an Inspection Robotic System for Indoor Applications

The inspection and monitoring of industrial sites, structures, and infrastructure are important issues for their sustainability and further maintenance. Although these tasks are repetitive and time consuming, and some of these environments may be characterized by dust, humidity, or absence of natural light, classical approach relies on large human activities. Automatic or robotic solutions can be considered useful tools for inspection because they can be effective in exploring dangerous or inaccessible sites, at relatively low-cost and reducing the time required for the relief. The development of a paradigmatic system called Inspection Robotic System (IRS) is the main objective of this paper to demonstrate the feasibility of mechatronic solutions for inspection of industrial sites. The development of such systems will be exploited in the form of a tool kit to be flexible and installed on a mobile system, in order to be used for inspection and monitoring, possibly introducing high efficiency, quality and repetitiveness in the related sector. The interoperability of sensors with wireless communication may form a smart sensors tool kit and a smart sensor network with powerful functions to be effectively used for inspection purposes. Moreover, it may constitute a solution for a broad range of scenarios spacing from industrial sites, brownfields, historical sites or sites dangerous or difficult to access by operators. First experimental tests are reported to show the engineering feasibility of the system and interoperability of the mobile hybrid robot equipped with sensors that allow real-time multiple acquisition and storage

Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected

Toward Autonomous Wall-Climbing Robots for Inspection of Concrete Bridges and Tunnels

In addition to visual inspection for surface flaws, inspectors are often required to detect subsurface defects (e.g., delamination and voids) using nondestructive evaluation (NDE) instruments, such as ground penetration radar (GPR) and impact sounding device, in order to determine the structural integrity of bridges and tunnels. In these cases, access to critical locations for reliable and safe inspections is a challenge. Since 2002, Dr. Jizhong Xiao’s group has developed four generations of wall-climbing robots for NDE inspection of bridges and tunnels. These robots combine the advantages of aerodynamic attraction and suction to achieve a desirable balance of strong adhesion and high mobility. For example, Rise-Rover with two drive modules can carry up to 450 N payload, and GPR-Rover can carry a small GPR antenna for subsurface flaw detection and utility survey on concrete structures. These robots can reach difficult-to-access areas (e.g., the bottom side of bridge decks), take close-up pictures, record and transmit NDE data to a host computer for further analysis. They can potentially make bridge inspection faster, safer, and cheaper without affecting traffic flow on roadways. This presentation will review the recent development of smart and autonomous wall-climbing robots to realize automated inspection of civil infrastructure with minimal human intervention

Computer vision-based method for concrete crack detection

© 2016 IEEE. This paper presents a computer vision-based method to automatically detect concrete cracks. We focus on images containing the concrete: background and crack, where the background is the major mode of the gray-scale histogram. Therefore, we address the detection problem of potential concrete cracks by dealing with histogram thresholding to extract regions of interests from the background. We first employ line emphasis and moving average filters to remove noise from concrete surface images obtained from an inspection robot. The developed algorithm is then applied for automatic detection of significant peaks from the gray-scale histogram of the smoothed image. The biggest peak and its corresponding valley(s) are consequently identified to calculate the threshold value for image binarization. The effectiveness of our proposed method was successfully evaluated on various test images, where cracks could be identified without the requirement of some heuristic reasoning