2022· International Journal of Intelligent Automation & Robotics Engineering· 0 citations
TL;DR
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 and provides a scalable, energy-efficient, and intelligent monitoring solution suitable for next-generation smart factories and Industry 5.0 environments.
Abstract
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.
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
The Fourth Industrial Revolution is accelerating the adoption of Industry 4.0 through intelligent computing, Industrial Internet of Things (IIoT), edge computing, and AI-driven automation. Traditional cloud-based industrial systems often experience latency, bandwidth limitations, network congestion, and privacy concerns, making them unsuitable for real-time manufacturing applications. This paper proposes an Edge Intelligence framework that integrates distributed edge computing, real-time AI analytics, and autonomous decision-making to process industrial IoT data locally. The architecture consists of four layers: perception, edge intelligence, autonomous decision, and cloud coordination. Industrial sensors, PLCs, and robotic systems collect operational data, while lightweight machine learning, deep learning, and reinforcement learning models perform feature extraction, anomaly detection, predictive analytics, and adaptive control with minimal latency. A mathematical optimization model minimizes processing delay, energy consumption, and resource utilization while maximizing accuracy and efficiency. Experimental evaluation demonstrates improved real-time performance, decision accuracy, fault detection, scalability, and resource utilization, making the framework well suited for next-generation smart manufacturing and sustainable industrial automation.
V. Sethi· International Journal of Int...· 0 citations
This paper proposes a unified CPPS-based framework that integrates intelligent sensing, cyber-physical communication, distributed computing, autonomous decision support, adaptive robotic control, predictive maintenance, and real-time production optimization, and establishes CPPS as a robust foundation for sustainable, resilient, and intelligent autonomous factories.
Michael Rabin, Amir Pnueli· International Journal of Int...· 0 citations
The rapid evolution of intelligent robotic systems has increased the demand for embedded computing architectures capable of performing complex perception, decision-making, and control operations under strict energy constraints. Traditional robotic platforms often rely on centralized cloud computing or high-performance processors, resulting in increased communication latency, energy consumption, and reduced autonomy. Energy-aware embedded intelligence overcomes these drawbacks by directly embedding lightweight artificial intelligence (AI) algorithms, hardware accelerators, adaptive power management and edge-based decision-making capabilities into robotic platforms. We propose an energy-smart embedded intelligence framework for a smart robotic application, engaging the ensemble of (i) embedded AI (ii) sensor fusion mechanism (iii), Nature-mimicry based energy optimization strategy and (iv) real-time autonomous control. We have designed robotics based on dynamic workload scheduling and intelligent resource allocation for dynamically balancing the computational performance with power consumption. The paper introduces the results of an analysis on state-of-the-art in embedded intelligence methods, short description of research gaps and also insight regarding energy-efficient architectures effectiveness that addresses mobile robots, autonomous robotic systems, and more broadly industrial robots. Comparative evaluation shows that energy-aware embedded intelligence can drastically reduce energy consumption and maintain high accuracy and responsiveness. It suggests that enhanced edge-intelligence architectures will drive sustainable, autonomous, and scalable operation of next-generation robotic systems in Industry 5.0 settings.
Narendra Karmarkar· International Journal of Int...· 0 citations
The rapid evolution of Industry 5.0 has accelerated the integration of intelligent automation, artificial intelligence (AI), Industrial Internet of Things (IIoT), and cyber-physical systems into modern manufacturing environments. One of these newly developed technologies, DT technology has recently emerged as one of the transformational paradigms in realising predictive intelligence, autonomous maintenance and online operational optimisation of robotic systems. Conventional robotic maintenance strategies, such as corrective and preventive maintenance often lead to unexpected downtimes, unnecessary resources allocation and inflated maintenance costs largely due to the nature of scheduled inspections or post-failure interventions. The maintenance frameworks enabled by Digital Twin overcome these limitations as they deterministically establish a dynamically-updated virtual representation of physical robotic assets connecting sensor networks, cloud-edge computing, AI analytics and real-time simulation. This paper proposes a full Digital Twin-based autonomous robotic maintenance framework along with data acquisition from multiple sensors, edge intelligence, machine learning (ML)-based diagnosis and prediction of failures and autonomous decision-making for predictive maintenance. The proposed framework supports continuous monitoring of health, anomaly detection, RUL prediction and adaptive maintenance scheduling with minimal operational disruptions. A comparative analysis reveals that Digital Twin-assisted maintenance not only increases fault detection precision, maintenance efficiency, system availability, and operational reliability compared to traditional practices. It further discusses the current technological challenges, research gaps, and avenues for future work relating to federated Digital Twins, explainable AI (XAI), collaborative robotics, and sustainable intelligent maintenance systems. This framework lays a strong, scalable foundation for Industry 5.0 ecosystems of next generation autonomous robotic maintenance in the smart factory.
O. Olesen· International Journal of Int...· 0 citations
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.
Z. Pawlak, Jan Łukasiewicz· International Journal of Int...· 0 citations
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