Design of Tunnel Inspection Robot for Large Diameter Sewers

Seet, Gerald; Yeo, Song Huat; Law, Wei Chuan; Burhan,; Wong, Choon Yue; Sapari, S.; Liau, K.K.

doi:10.1016/j.procs.2018.07.078

Cited by 17 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the safety and accessibility issues, robotic technologies have also been developed and used for inspection instead of manual labor. For example, robots have been used to remotely monitor and inspect hazardous areas in different types of power plants [1,2]. In another study [3], a monorail-type compact inspection robot equipped for acoustic and thermal inspection and, in another [4], an underwater inspection robot with various forms of wireless communication means for visual inspection of nuclear power plant facilities have been developed.…”

Section: Introductionmentioning

confidence: 99%

Autonomous aerial flight path inspection using advanced manufacturing techniques

et al. 2022

View full text Add to dashboard Cite

SUMMARY Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structuralcomponents.

show abstract

Section: Introductionmentioning

confidence: 99%

Autonomous aerial flight path inspection using advanced manufacturing techniques

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Digital technologies have assisted maintenance planning, monitoring, and inspection for decades. Some of these digital technologies include the use of image processing techniques (Kubo et al, 1995), robotics (Seet et al, 2018), aerial vehicles (Nikolic et al, 2013), and computer vision (Dung and Le Duc, 2018). Computational vision-based methods of surface defect detection-via machine learning and convolutional neural networks (CNN)-have been extensively researched in recent years (Rawat and Wang, 2017;Garfo et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Crack Detection in Infrastructure using Computer Vision at the Edge

Flores-Abad

Rizia

Reyes-Munoz

et al. 2022

Preprint

View full text Add to dashboard Cite

Automatic real-time detection of structural damages on site is a required technology to enable rapid, accurate, and on-site inspection. This paper introduces an automated intelligent inspection system capable of detecting structural problems, such as cracks, in real-time at the edge of power plant components. Since no available dataset was suitable for this case study, a real dataset was created by combining new and existing. For inspection, this project customized a Deep Neural Network (DNN) model to fit our application, including its quantization to enable deployment at the edge. Real-time, on-site results from aerial and hand-held setup images of the stack of an old power plant show that the system is capable of identifying and localizing cracks within the field of view (FOV) of the camera with a mean average precision (mAP) of 98.44% and ∼ at 2.5 frames per second (FPS) with real-time inference for crack detection at the edge.

show abstract

“…Redzone Robotics' Responder is a 300‐kg, tracked platform which can be deployed in sewers with a diameter from 915 to 6,000 mm and has a reach of up to 1,000 m. The robot is equipped with cameras, laser sensors, and ultrasonic sensors. A robot developed by Nanyang Technological University can be passed through tunnels with a minimum diameter of 3,000 mm and has explored distances of up to 400 m (Seet et al, 2018). The robot uses cameras and a laser profiler.…”

Section: Introductionmentioning

confidence: 99%

Towards autonomous inspection of concrete deterioration in sewers with legged robots

Kolvenbach

Wisth

Buchanan

et al. 2020

Journal of Field Robotics

View full text Add to dashboard Cite

The regular inspection of sewer systems is essential to assess the level of degradation and to plan maintenance work. Currently, human inspectors must walk through sewers and use their sense of touch to inspect the roughness of the floor and check for cracks. The sense of touch is used since the floor is often covered by (waste) water and biofilm, which renders visual inspection very challenging. In this paper, we demonstrate a robotic inspection system which evaluates concrete deterioration using tactile interaction. We deployed the quadruped robot ANYmal in the sewers of Zurich and commanded it using shared autonomy for several such missions. The inspection itself is realized via a well-defined scratching motion using one of the limbs on the sewer floor. Inertial and force/torque sensors embedded within specially designed feet captured the resulting vibrations. A pretrained support vector machine (SVM) is evaluated to assess the state of the concrete. The results of the classification are then displayed in a three-dimensional map recorded by the robot for easy visualization and assessment. To train the SVM we recorded 625 samples with ground truth labels provided by professional sewer inspectors. We make this data set publicly available. We achieved deterioration level estimates within three classes of more than 92% accuracy. During the four deployment missions, we covered a total distance of 300 m and acquired 130 inspection samples.

show abstract

Design of Tunnel Inspection Robot for Large Diameter Sewers

Cited by 17 publications

References 7 publications

Autonomous aerial flight path inspection using advanced manufacturing techniques

Autonomous aerial flight path inspection using advanced manufacturing techniques

Intelligent Crack Detection in Infrastructure using Computer Vision at the Edge

Towards autonomous inspection of concrete deterioration in sewers with legged robots

Contact Info

Product

Resources

About