Robotic Inspection System Research Articles

Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection

Purpose This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork. Design/methodology/approach This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period. Findings Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers. Originality/value The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Research on abnormal object detection network of computer room inspection robot based on depth vision

Abstract This paper investigates a deep learning-based anomaly object detection network for identifying and alerting on abnormal items within computer room. First, the framework of the data center inspection robot system is outlined, and the anomaly detection task is decomposed. Next, a dataset of abnormal objects based on data center environmental information is established, and augmentation operations are performed on the created dataset. Subsequently, a SqueezeNet network model based on Residual Squeeze Excitation and Atrous Spatial Pyramid Pooling (RSE-ASPP) is proposed to optimize and improve the SqueezeNet network model. Finally, this paper employs transfer learning to address the issue of insufficient data volume. By pre-training on a large-scale dataset and fine-tuning on the constructed dataset, the accuracy and stability of abnormal object recognition can be significantly enhanced. Ultimately, the proposed RSE-ASPP-SqueezeNet network achieves high-precision detection of abnormal items in the data center inspection robot's anomaly detection task.

Design and Analysis of an Adaptive Pipeline Detection and Correction Mechanism

This paper presents a novel adaptive pipeline probe detection and correction mechanism designed to address the challenge of detection interference caused by the movement of wall-climbing robots, particularly in complex environments such as water-cooled walls. The mechanism ensures that the detection probe can accurately detect individual pipelines even when the robot deviates from its intended path. To achieve this, the system incorporates a self-adaptive deviation correction mechanism that maintains consistent detection performance without requiring adjustments to the robot's spatial position. The design includes a variable stiffness analysis of the buffer spring within the correction mechanism, which is optimized to minimize the impact of the robot's movement on the detection components. By carefully selecting the spring's size and stiffness parameters, the mechanism reduces vibration and enhances the stability and reliability of pipeline detection under offset conditions. In addition to maintaining detection accuracy, the system also supports automatic marking of pipelines that exhibit quality issues, ensuring that any detected defects are easily traceable. This adaptive mechanism not only improves detection efficiency but also enhances the overall operational stability of wall-climbing robots in industrial inspection tasks. The results demonstrate the mechanism's effectiveness in mitigating the challenges posed by uneven friction and time delays in the control system, making it a significant contribution to the field of robotic inspection systems.

Ontology for BIM-Based Robotic Navigation and Inspection Tasks

The availability of inspection robots in the construction and operation phases of buildings has led to expanding the scope of applications and increasing technological challenges. Furthermore, the building information modeling (BIM)-based approach for robotic inspection is expected to improve the inspection process as the BIM models contain accurate geometry and relevant information at different phases of the lifecycle of a building. Several studies have used BIM for navigation purposes. Also, some studies focused on developing a knowledge-based ontology to perform activities in a robotic environment (e.g., CRAM). However, the research in this area is still limited and fragmented, and there is a need to develop an integrated ontology to be used as a first step towards logic-based inspection. This paper aims to develop an ontology for BIM-based robotic navigation and inspection tasks (OBRNIT). This ontology can help system engineers involved in developing robotic inspection systems by identifying the different concepts and relationships between robotic inspection and navigation tasks based on BIM information. The developed ontology covers four main types of concepts: (1) robot concepts, (2) building concepts, (3) navigation task concepts, and (4) inspection task concepts. The ontology is developed using Protégé. The following steps are taken to reach the objectives: (1) the available literature is reviewed to identify the concepts, (2) the steps for developing OBRNIT are identified, (3) the basic components of the ontology are developed, and (4) the evaluation process is performed for the developed ontology. The semantic representation of OBRNIT was evaluated through a case study and a survey. The evaluation confirms that OBRNIT covers the domain’s concepts and relationships, and can be applied to develop robotic inspection systems. In a case study conducted in a building at Concordia University, OBRNIT was used to support an inspection robot in navigating to identify a ceiling leakage. Survey results from 33 experts indicate that 28.13% strongly agreed and 65.63% agreed on the usage of OBRNIT for the development of robotic navigation and inspection systems. This highlights its potential in enhancing inspection reliability and repeatability, addressing the complexity of interactions within the inspection environment, and supporting the development of more autonomous and efficient robotic inspection systems.

Design of robot inspection system for large converter station

Design of robot inspection system for large converter station

Evaluation of a Smart Mobile Robotic System for Industrial Plant Inspection and Supervision

Evaluation of a Smart Mobile Robotic System for Industrial Plant Inspection and Supervision

Exploration of using a wall-climbing robot system for indoor inspection in occupied buildings

Indoor inspection robots operating in occupied buildings need to minimize disturbance to occupants and access high areas of a room and cramped spaces obstructed by obstacles for higher inspection coverage. However, existing indoor inspection robots are still unable to meet these requirements. This paper aims to explore the feasibility of applying wall-climbing robots to address these requirements. To this end, we propose a small-sized wall-climbing robot prototype that can move on common indoor surfaces. We extend the proposed prototype to support thermographic inspection by integrating thermal imaging technology into it. Experiment results show that the proposed robot prototype can reach more wall and floor areas for inspection than previously developed indoor inspection robots. It has also been demonstrated that the reduced size and the wall-climbing ability allow the robot to largely avoid human activity areas, thereby reducing disturbance to occupants. This study represents the first attempt to introduce wall-climbing robots into the indoor inspection domain and provides the initial validation of their advantages over existing indoor inspection robots regarding improving inspection coverage and minimizing disturbance to occupants. The findings in this study can provide valuable insights for the future design, selection and application of robotic systems for indoor inspection tasks.

Integrating artificial intelligence with SLAM technology for robotic navigation and localization in unknown environments

In the era of advancing technology, unmanned inspection robots have become indispensable for their efficiency, precision, and safety. Key to their autonomous operation is Simultaneous Localization and Mapping (SLAM) technology, which allows robots to navigate and create maps of unknown environments in real-time. This article explores the integration of SLAM with artificial intelligence, highlighting its role in robotic navigation, localization, and obstacle avoidance. Specifically, we delve into SLAM's principles, its implementation with LiDAR technology, and its application in autonomous robot localization. Furthermore, we introduce a collaborative mapping algorithm based on ORB-SLAM3, enhancing map construction efficiency and real-time performance. Through our exploration, we illustrate the transformative potential of SLAM technology, paving the way for safer and more efficient robotic inspection systems across various industries.

Development of an In-Pipe Inspection Robot for Large-Diameter Water Pipes.

This paper describes the development of an in-pipe inspection robot system designed for large-diameter water pipes. The robot is equipped with a Magnetic Flux Leakage (MFL) sensor module. The robot system is intended for pipes with diameters ranging from 900 mm to 1200 mm. The structure of the in-pipe inspection robot consists of the front and rear driving parts, with the inspection module located centrally. The robot is powered by 22 motors, including eight wheels with motors positioned at both the bottom and the top for propulsion. To ensure that the robot's center aligns with that of the pipeline during operation, lifting units have been incorporated. The robot is equipped with cameras and LiDAR sensors at the front and rear to monitor the internal environment of the pipeline. Pipeline inspection is conducted using the MFL inspection modules, and the robot's driving mechanism is designed to execute spiral maneuvers while maintaining contact with the pipeline surface during rotation. The in-pipe inspection robot is configured with wireless communication modules and batteries, allowing for wireless operation. Following its development, the inspection robot underwent driving experiments in actual pipelines to validate its performance. The field test bed used for these experiments is approximately 1 km in length. Results from the driving experiments on the field test bed confirmed the robot's ability to navigate various curvatures and obstacles within the pipeline. It is posited that the use of the developed in-pipe inspection robot can reduce economic costs and enhance the safety of inspectors when examining aging pipes.

Development of mobile minirobots for in pipe inspection tasks

The possibilities to use a miniaturized robotic system for inspection, exploration and maintenance of the pipes are highlighted. In this paper, the authors’ contributionin this field is discussed. Two in-pipe minirobots developed by the authors are described. The efforts are focused on the in-pipe mobility and corresponding actuationsystems. Several functional characteristics (e.g. the maximum /minimum inner pipe diameter) are given.

Deep spatiotemporal fusion network for vision-based robotic inspection of structures

The convolutional neural networks commonly deployed for semantic understanding of visual inspection data can, in general, learn robust spatial features. However, they lack the ability to capture temporal dependencies that characterize the video data collected by various robotic inspection systems. As a result, they are found lacking in dealing with various challenges arising from cross-view illumination variation, perspective difference, scale change, background clutter, and occlusion. Their performance is further deteriorated by motion blur and other distortions induced by rapid camera movement. This study aims to address this challenge by extending the task of visual scene understanding from the still image domain to the video domain by incorporating cross-frame information fusion. A deep end-to-end network is developed by integrating an encoder–decoder-based convolutional neural network with a long short-term memory-based recurrent neural network for pixel-level semantic labeling of sequential visual inspection data. The proposed multishot architecture can jointly learn discriminative fusion features leading to a rich understanding of the complex spatiotemporal dynamics. The proposed approach is validated with two case studies involving automatic structural element segmentation in robotic building and bridge inspection videos. Two different multishot fusion techniques are suggested leveraging sequence-to-one and sequence-to-sequence architectures. Additionally, two different fusion schemes based on the sum-of-scores and Bayesian updating rules are examined to aggregate multiple label maps produced at each time step by an overlapping sliding window-based inference scheme. A comprehensive performance evaluation indicated that multishot fusion could enhance the intersection over union (IoU) score by 4.6% and 13.3% for building and bridge component segmentation tasks, respectively, compared to a baseline single-shot approach.

Design of Community Inspection Robot System

With the continuous development of the global economy, urban centers have experienced a consistent increase in size. Consequently, the number of schools, parks and residential communities within cities are also on the rise. The increasing quantity of various types of communities has juxtaposed a notable dilemma: a shortage of security and maintenance personnel. In order to address this issue, employing robotic automated inspection within these communities, coupled with autonomous navigation, obstacle avoidance, and AI recognition technology, is proposed. This project aims to alleviate the burden on human staff, improve recognition accuracy, and enhance the overall efficiency of operation and maintenance. This paper analyzes the operational needs of these communities, presents a design concept for a community inspection robot system, and discusses issues that require further research in the future.

Verification and Validation of Camera Based Robotic Inspection System via Camera Fault Injection Tool

Günümüzde, gelişen görüntü işleme teknikleri ile birlikte kamera tabanlı robotik inceleme sistemleri oldukça popülerlik kazanmıştır. Bu tür sistemler gıdadan, askeriyeye birçok sektörde yoğun olarak kullanılmaktadır. Bu sistemler geliştirilirken gerekli olan doğrulama ve onaylama süreçleri oldukça uzun ve maliyetli olmaktadır. Bu çalışma, kamera tabanlı endüstriyel robotik sistemler üzerinde doğrulama ve onaylama faaliyetlerini gerçekleştirmek ve iyileştirmek amacıyla geliştirilmiştir. RGB ve TOF kameralara farklı türlerde (Open, Close, Dilation, Erosion, Gradient, Motionblur, Tuz&Biber, Gaussian ve Poisson) hata enjeksiyon yöntemleri kullanılmasını mümkün Kamera Hata Enjeksiyon Aracı (CamFITool) ile gerçekleştirilmiş testler ve sonuçlar açıklanmıştır. Yapılan çalışma, VALU3S projesi kapsamında, OTOKAR’ın ROKOS robotik sistemine, CamFITool ile gerçek ortamdan alınmış kamera görüntülerinden oluşan kitaplıklara, çeşitli konfigürasyonlarda hatalar enjekte edilip, bu enjeksiyonun sisteme etkilerinin incelenmesine odaklanmıştır. Bu kapsamda 49 farklı test komfigürasyonunda hata enjeksiyonu gerçekleştirilmiştir. Sonuç olarak, kamera tabanlı endüstriyel robotik sistemlerin daha güvenli ve stabil çalışmalarının sağlanması için, bu sistemlerin hataya dayanıklı olup olmadıklarını test eden açık kaynaklı bir hata enjeksiyon aracı olan CamFITool önerilmiştir.

Robust Geometry Self-Calibration Based on Differential Kinematics for a Redundant Robotic Inspection System

Robust Geometry Self-Calibration Based on Differential Kinematics for a Redundant Robotic Inspection System

A Robot System for Rapid and Intelligent Bridge Damage Inspection Based on Deep-Learning Algorithms

A Robot System for Rapid and Intelligent Bridge Damage Inspection Based on Deep-Learning Algorithms

An Intelligent Equipment Inspection System based on Robotic Arm

Intelligent robots have replaced manual labor to complete a lot of complicated work, especially in equipment inspection has advantages. The purpose of this paper is to design an intelligent robotic inspection system. The system is based on ROS and adopts YOLO algorithm, RRT* algorithm, Agile_grasp function package, and MoveIt! framework to realize the basic target recognition, position recognition, robotic arm navigation planning and other functions. The human-computer interaction interface has been designed, and the simulation test in Gazebo and the real machine test in real environment have also been completed. The system still has a large space for improvement, and further research will be conducted in the future in terms of response speed and accuracy.

Super-twisting sliding mode finite time control of power-line inspection robot with external disturbances and input delays

An adaptive super-twisting sliding mode control (AST-SMC) is proposed for the power-line inspection (PLI) robot system with two degrees of freedom and single control input underactuated with multiple balances. Firstly, the nonlinear dynamic equation of PLI robot is established, and the model is linearized at the nominal equilibrium point. Secondly, a special transformation is introduced to transform the original input delay system into a delay-free model, and an equilibrium manifold linearization (EML) model is established by using scheduling variables to expand the operating range of the controlled system, as a result of which the disturbance observer control (DOC) is designed to estimate the external disturbance. Thirdly, in order to achieve fast convergence and continuous control, the super-twisting sliding mode control(ST-SMC) method is proposed to decrease chattering in the control law. Finally, it can be verified by Lyapunov stability theory that the system reaches the sliding mode surface in finite time and the trajectory tracking error converges to zero in finite time. The simulation results verify that the combination of DOC and AST-SMC has a optimal disturbance approximation and control convergence ability, clarifying the effectiveness and robustness of the proposed control scheme.

Auto-Inspection System Using Optimized Fuzzy Sliding Mode Control Strategy for Tunnel Inspection

Cities composed of many mountainous areas necessitate the use of many tunnels for roads and highways with a potential safety hazard. To determine the safety of tunnels, periodic tunnel inspections mainly rely on manual work, which is dangerous and slow. Therefore, this paper proposes an auto-inspection system for tunnel inspection consisting of a robotic arm, laser sensors, and an inspection radar to free inspectors from hazardous environments and high-intensity work. Based on the mathematical model in the inspection process, a sliding mode controller is designed and optimized with fuzzy control and hyperbolic tangent functions, and is used in a tunnel inspection robot system for the first time. The simulation results show that optimized fuzzy sliding mode control can improve the tracking accuracy and stability during the tracking process compared with the traditional algorithm. Curved line inspection and arch line inspection experiment tests demonstrate that our system can automatically inspect the tunnel, and that the optimized fuzzy sliding mode control provides a superior performance in terms of the tracking process, with the average error decreasing by 37.8% when compared to traditional algorithms. Therefore, the proposed system is of great significance for high-precision and high-stability unmanned automated tunnel inspection.

An autonomous rail-road amphibious robotic system for railway maintenance using sensor fusion and mobile manipulator

The current maintenance of railway infrastructure replies heavily on human involvement, requiring possession of the track section during maintenance, resulting in high costs and inefficient execution. This paper proposes an autonomous rail-road amphibious robotic system for railway inspection and maintenance tasks. By virtue of its road and rail-autonomous mobility, it is able to execute the complete maintenance execution flow in multiple phases. The system provides flexible track job location access, low-cost maintenance execution, and reduced track network possession. The payload mobile manipulator and sensor fusion enhance the system's capabilities for multiple types of inspection and repair. The design of a command and control system was guided by a rule-based expert system strategy to enable remote operation of the whole system. The developed demonstrator of a track wheel accompanied unmanned ground vehicle was integrated and demonstrated in both operational and realistic track environments with multiple testing activities of remote operation, navigation, accurate job detection, inspection, and repair, confirming effective job completion and logical human interaction. The proposed method produces an outstanding hardware-software integrated robotic inspection and repair system with a high level of technological readiness for autonomous railway maintenance and intelligent railway asset management.

Robot Path Planning Method Combining Enhanced APF and Improved ACO Algorithm for Power Emergency Maintenance

Considering the limited adaptability of the existing substation inspection robot path planning (PP) algorithms, the authors propose a novel PP method for mobile robots (MR) based on the structure of the ultra-high voltage (UHV) substation inspection robot system. The proposed method combines the improved ant colony optimization (IACO) algorithm and the enhanced artificial potential field (EAPF) algorithm. To minimize the interference of the pheromones, they introduced a pheromone adjustment coefficient in the later iterations of the algorithm. Furthermore, they improved the global pheromone update method, which is beneficial to the MR to search for the optimal path (OP) rapidly. They constructed two environmental models using the grid method, and they used MATLAB to implement comparative experiments between the proposed algorithm and other advanced methods. The results demonstrate that the proposed algorithm outperforms other methods in terms of running time, convergence speed, and global optimization ability.