2024· International Journal of Modern Research in Science & Engineering· Vol 7, pp. 01-18· 0 citations
TL;DR
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.
Abstract
Industrial environments are rapidly evolving toward Industry 4.0, where autonomous robots and AI enable intelligent inspection and maintenance with minimal human intervention. Traditional inspection methods rely on manual operations, fixed robotic programming, and isolated computer vision techniques that struggle in dynamic industrial environments. This work proposes FACTS, an autonomous industrial inspection and maintenance framework integrating robotics with vision-language AI. The framework combines multimodal sensing, advanced computer vision models (CNNs, Vision Transformers, and vision-language foundation models), and natural language understanding to interpret equipment conditions, detect defects, reason about maintenance requirements, and execute autonomous corrective actions. Vision-language feature fusion enhances contextual understanding, while mathematical optimization improves decision accuracy and computational efficiency. The proposed system supports applications in manufacturing, power plants, aerospace, and critical infrastructure by enabling defect detection, predictive maintenance, safety monitoring, and remote assistance. 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.
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.
Arvind Kumar Singh, Lakshmi Narayanan· International Journal of Int...· 0 citations
The rapid evolution of Industry 4.0 has accelerated the adoption of autonomous robotic systems in intelligent manufacturing. Traditional robot task planning methods are limited in dynamic production environments due to their dependence on rule-based programming and deterministic algorithms. In contrast, Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs) and multimodal foundation models, enables robots to understand natural language, perform contextual reasoning, generate adaptive task plans, and respond intelligently to changing industrial conditions. This paper presents a Generative AI-based framework for robotic task planning that integrates multimodal perception, semantic reasoning, LLM-based planning, reinforcement learning, and execution monitoring. The framework enhances planning accuracy, adaptability, collaboration, and operational efficiency while reducing execution errors and planning complexity. It is applicable to smart assembly, automated inspection, warehouse logistics, predictive maintenance, collaborative robotics, and flexible manufacturing. The study concludes that Generative AI offers a promising foundation for scalable, adaptive, and human-centric industrial automation, with future research directed toward continual learning, explainable AI, federated robotic intelligence, edge AI, and trustworthy autonomous decision-making.
Narendra Karmarkar· International Journal of Int...· 0 citations
Intelligent embedded vision systems have emerged as a fundamental enabling technology for autonomous machines operating in industrial automation, autonomous vehicles, service robotics, agricultural robotics, healthcare systems, and smart surveillance. Unlike conventional machine vision systems that rely on centralized cloud processing, embedded vision integrates cameras, processors, artificial intelligence (AI), and edge computing into compact hardware platforms capable of performing real-time perception and decision-making. Now, the use of new advancements, such as deep learning, computer vision, edge AI accelerators and low-power embedded processors have vastly improved object detection accuracy, scene understanding and autonomous navigation at lower latencies and energy consumption. However, the embedded vision systems still have problems with locked in computational limitation, environmental variations, reliability of other sensors as well as cybersecurity and explainability of AI decisions. Abstract: This study provides a comprehensive overview and a conceptual framework for intelligent embedded vision systems of autonomous machines. As such, our architecture combines several components that include embedded cameras, AI on edge, sensor fusion, deep neural networks and adaptive control to optimize perception and operational intelligence. The results show that the proposed framework is comparably better than classical embedded vision architectures in terms of object recognition, inference time, navigation accuracy and energy consumption. This paper also pinpoints gaps and future opportunities with respect to current research including explainable AI, neuromorphic computer, federated learning and digital twin technologies. The results provide researchers and practitioners with insights into designing the next-generation autonomous robotic platforms to be ready for Industry 5.0 environment.
Z. Pawlak, Jan Łukasiewicz· International Journal of Int...· 0 citations
The rapid advancement of Industry 4.0 and intelligent manufacturing has significantly increased the adoption of industrial robots in automated production environments. Modern robotic systems require continuous monitoring to ensure operational efficiency, safety, reliability, and predictive maintenance. Conventional monitoring systems primarily rely on centralized cloud processing and threshold-based diagnostics, which often introduce communication latency, increased bandwidth consumption, and limited responsiveness during critical operational events. Real-time embedded artificial intelligence (AI) has emerged as an effective solution by enabling intelligent data processing directly at the edge using embedded processors integrated within robotic platforms. This study presents a comprehensive research framework for real-time embedded AI-based industrial robot monitoring that combines edge computing, embedded deep learning, multi-sensor fusion, anomaly detection, predictive maintenance, and intelligent decision-making. The proposed architecture integrates vision sensors, vibration sensors, temperature sensors, current sensors, and inertial measurement units with embedded AI accelerators to perform low-latency inference and continuous robot health assessment. A comparative analysis demonstrates that embedded AI significantly improves monitoring accuracy, reduces response time, minimizes communication overhead, and enhances predictive maintenance capability compared with conventional cloud-based monitoring systems. Furthermore, the study discusses research gaps, implementation challenges, and future opportunities involving federated learning, digital twins, explainable AI, and collaborative industrial robotics. The proposed framework provides a scalable, energy-efficient, and intelligent monitoring solution suitable for next-generation smart factories and Industry 5.0 environments.
Corrado Böhm, Corrado Gini· International Journal of Int...· 0 citations
Autonomous robotic platforms have become an integral part of industrial automation, intelligent manufacturing, autonomous transportation, precision agriculture, healthcare robotics, and hazardous environment exploration. The operational reliability of these robotic systems strongly depends on the accuracy and health of their sensing infrastructure. Sensors including inertial measurement units (IMUs), LiDAR, cameras, ultrasonic sensors, GPS modules, encoders, force-torque sensors, and proximity sensors continuously provide environmental and operational information for autonomous decision-making. However, sensor degradation, calibration drift, communication failures, environmental interference, and hardware aging can significantly deteriorate robotic performance and may even lead to catastrophic failures. Consequently, intelligent sensor fault diagnosis has emerged as a critical research area that combines artificial intelligence, machine learning, data analytics, and model-based reasoning to identify, classify, and predict sensor faults in real time. In this paper, a complete intelligent sensor fault diagnosis framework for autonomous robotic platforms is proposed. The proposed framework utilizes the integration of multi-sensor data fusion, feature extraction, deep learning-based fault classification, anomaly detection, and predictive maintenance components into a unified architecture. Experimental performance evaluation shows significant enhancements versus traditional threshold-based diagnostic approaches in terms of high fault detection accuracy, lower false alarms and reduction in the latency for diagnosis among different faults with an increased reliability level on system diagnosis. These results demonstrate that AI-assisted diagnostic models significantly increased robotic autonomy through the ability to proactively manage faults, reduce downtime and enhance operational safety. The proposed framework is suitable for deployment in Industry 5.0 manufacturing systems, autonomous vehicles, collaborative robots, and intelligent service robotics.
James Carter, Patricia Hall· International Journal of Int...· 0 citations
Abstract: The AI-Powered Autonomous Multi-Functional Robot is an intelligent robotic system that integrates Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, and the Internet of Things (IoT) to perform multiple tasks autonomously. The system is designed to reduce human intervention while improving operational efficiency, accuracy, and safety in dynamic environments. It uses advanced sensors and AI algorithms for real-time object detection, obstacle avoidance, autonomous navigation, and environmental monitoring. IoT connectivity enables remote monitoring, control, and cloud-based data management. The robot can be applied in industrial automation, healthcare, agriculture, smart homes, logistics, security surveillance, and disaster management. Its modular architecture ensures scalability and supports future enhancements with emerging technologies. Overall, the proposed system provides a reliable, cost-effective, and intelligent automation solution capable of addressing real-world challenges while contributing to the advancement of next-generation autonomous robotic systems.
Keywords: Artificial Intelligence (AI), Autonomous Robot, Machine Learning (ML), Computer Vision, Internet of Things (IoT), Autonomous Navigation, Object Detection, Obstacle Avoidance, Environmental Monitoring, Intelligent Decision-Making, Smart Automation, Industrial Robotics, Sensor Fusion, Cloud Connectivity, Real-Time Monitoring.
P. Priya, Paripalli Mahalakshmi· International Scientific Jou...· 0 citations