2022· International Journal of Intelligent Automation & Robotics Engineering· 0 citations
TL;DR
The proposed framework integrates robotic navigation, visual sensing, image processing, defect classification, and maintenance decision support to achieve reliable inspection across diverse industrial sectors and supports predictive maintenance, improves quality assurance, and advances smart manufacturing in Industry 4.0.
Abstract
Autonomous robotic surface inspection combines intelligent robotics with computer vision to enable accurate, real-time, and contactless detection of surface defects such as cracks, corrosion, scratches, dents, and coating degradation. Unlike manual inspection, it improves consistency, enhances safety, reduces inspection time, and lowers operational costs. By integrating AI, deep learning, edge computing, IIoT, and Digital Twin technologies, autonomous robots can navigate complex industrial environments, capture high-resolution images, and perform automated defect analysis with minimal human intervention. The proposed framework integrates robotic navigation, visual sensing, image processing, defect classification, and maintenance decision support to achieve reliable inspection across diverse industrial sectors. This approach supports predictive maintenance, improves quality assurance, and advances smart manufacturing in Industry 4.0.
Machine vision has become a key technology in modern industrial automation, enabling fully automated quality inspection in robotic manufacturing systems. Unlike traditional human inspection methods, machine vision offers higher accuracy, consistency, speed, and lower operational costs. These systems use cameras, lighting, lenses, image processing, pattern recognition, and AI techniques to detect defects, verify dimensions, classify products, and monitor manufacturing processes in real time. This survey reviews machine vision-based quality inspection methods in robotic manufacturing up to 2019, covering major applications in industries such as automotive, electronics, aerospace, pharmaceuticals, and food processing. It highlights advancements in feature extraction, defect detection algorithms, classification models, and robotic integration frameworks. The study shows that machine vision significantly improves inspection accuracy, reduces cycle time, and enhances manufacturing consistency, while also discussing challenges such as illumination changes, computational complexity, and system adaptability. Overall, machine vision plays a vital role in smart manufacturing and Industry 4.0 production systems.
Louis Pouzin, J. Arsac· International Journal of Int...· 0 citations
Experimental evaluation demonstrates significant improvements in inspection accuracy, fault classification, decision-making, maintenance prediction, reduced downtime, and lower human intervention, providing a foundation for next-generation intelligent industrial automation.
O. Dahl, K. Nygaard· International Journal of Mod...· 0 citations
A DNN-based vision-guided robotic assembly framework that integrates computer vision, intelligent decision-making, and real-time robotic control is proposed, supporting flexible automation and next-generation smart manufacturing in Industry 4.0 environments.
O. Dahl, K. Nygaard· International Journal of Int...· 0 citations
Computer vision (CV) systems driven by artificial intelligence (AI) are increasingly replacing manual and conventional rule-based inspection procedures in industrial quality inspection, enabling automated, real-time, and data-driven decision-making based on visual data. Conventional inspection procedures are usually limited in terms of scalability, human-related errors, and high operational costs, which drives the increasing reliance on smart vision-based technologies. A practical, practice-oriented review of AI-based computer vision systems for industrial quality control is provided in this paper, with emphasis on real-world deployment issues and performance aspects. Two representative industrial case studies are examined. The first investigates real-time extrusion monitoring in robotic building construction, where geometric deviations, bead-width variation, surface irregularities, and process inconsistencies are detected during material deposition using vision-based monitoring and image-processing pipelines. The second case study focuses on automated inspection of bolts and screws in manufacturing lines, addressing presence detection, orientation recognition, and defect classification under high-speed production conditions. In both cases, widely adopted vision and AI techniques, including image-processing pipelines, convolutional neural networks, and edge-computing hardware, are discussed and compared. The analysis shows that AI-enabled computer vision systems can outperform traditional rule-based or manual solutions in terms of inspection accuracy, consistency, and throughput when they are supported by reliable acquisition, representative data, and robust industrial integration. Nevertheless, challenges related to dataset quality, model generalization, lighting variability, and real-time computational constraints remain critical in industrial environments. In conclusion, AI-based computer vision plays a central enabling role in intelligent quality inspection within the context of Industry 5.0. Future research should focus on adaptive model capabilities, tighter integration with cyber-physical systems, and scalable deployment strategies to achieve reliable and autonomous inspection across diverse industrial sectors.
Maaz A. Khan, C. Vasques, A. Cavadas· Encyclopedia· 0 citations
Industry 4.0 technologies, including AI, IIoT, robotics, and cyber-physical systems, require industrial robots to maintain high positioning accuracy despite thermal, mechanical, and operational changes. Conventional offline calibration is time-consuming, costly, and unsuitable for dynamic manufacturing environments. The proposed autonomous calibration framework integrates multi-sensor fusion, machine vision, laser measurement, inertial sensing, machine learning, and adaptive optimization to continuously estimate and compensate for calibration errors in real time. By updating robot kinematic models during operation, the system improves positioning accuracy, repeatability, manufacturing quality, equipment utilization, and predictive maintenance while minimizing downtime and human intervention. Overall, the framework enables self-learning, real-time robotic calibration that supports intelligent, efficient, and sustainable smart manufacturing.
Seshagiri N· International Journal of Int...· 0 citations
Rapid urbanization, industrialization, and aging infrastructure have increased the need for efficient monitoring systems. Traditional manual inspections of bridges, tunnels, pipelines, dams, railway tracks, and industrial facilities are costly, time-consuming, labor-intensive, and risky. Autonomous inspection robots offer an advanced solution for smart infrastructure monitoring and maintenance. This study reviews autonomous inspection robots developed before February 2019, focusing on their design, navigation, sensors, communication systems, and control methods. These robots use technologies such as LiDAR, ultrasonic sensors, infrared cameras, thermal imaging, GPS, and wireless communication for real-time monitoring, defect detection, and predictive maintenance. Machine learning and computer vision further improve inspection accuracy. Different robot types, including wheeled, tracked, aerial, climbing, underwater, and hybrid robots, are compared based on mobility, adaptability, energy efficiency, and inspection performance. The paper also proposes an autonomous wheeled inspection robot using sensor fusion and computer vision for obstacle avoidance, wireless communication, and autonomous navigation. Results show that autonomous inspection robots improve safety, fault detection, and inspection efficiency compared to manual methods. Challenges such as power consumption, communication delays, localization errors, and sensor calibration are discussed. Future developments involving AI, IoT, cloud robotics, edge computing, swarm robotics, and digital twins are expected to enhance intelligent infrastructure monitoring systems.
Pooja Agarwal, Rakesh Chandra· International Journal of Int...· 0 citations
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