2022· International Journal of Intelligent Automation & Robotics Engineering· 0 citations
Abstract
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.
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.
Corrado Böhm, Corrado Gini· International Journal of Int...· 0 citations
Autonomous engineering systems require accurate perception, intelligent decision-making, and adaptive control to operate safely in dynamic environments. This paper proposes an AI-driven Intelligent Multi-Sensor Data Fusion Framework that integrates heterogeneous sensors with deep learning, probabilistic fusion, and reinforcement learning for enhanced environmental perception, autonomous reasoning, and real-time control. The framework consists of four layers: multi-sensor acquisition, intelligent preprocessing, AI-based fusion and reasoning, and autonomous execution. By employing adaptive confidence weighting and context-aware learning, it improves object detection, localization, fault diagnosis, decision reliability, and system robustness while addressing challenges such as sensor uncertainty, synchronization, failures, and cybersecurity. The scalable architecture is applicable to autonomous vehicles, robotics, smart manufacturing, infrastructure monitoring, and Industry 5.0 cyber-physical systems, with future research focusing on explainable AI, federated learning, digital twins, edge intelligence, and trustworthy autonomous decision-making.
Steven Young· International Journal of Mod...· 0 citations
Intelligent robotic navigation has become an important research area for autonomous systems operating in dynamic and uncertain environments. Before 2019, advancements in robotics, artificial intelligence, embedded systems, and wireless communication improved autonomous robots used in healthcare, agriculture, transportation, military, and service applications. Traditional single-sensor systems faced challenges such as low localization accuracy and poor environmental perception. To address these issues, Hybrid Wireless Sensor Networks (HWSNs) combined sensors like LiDAR, GPS, IMUs, cameras, ultrasonic sensors, and RFID to enhance navigation, obstacle detection, mapping, and reliability. Research focused on sensor fusion, SLAM, Kalman filtering, fuzzy logic, neural networks, machine learning, and real-time obstacle avoidance. The proposed hybrid framework supports accurate localization, autonomous mapping, and efficient path planning for indoor and outdoor environments. Experimental results show that hybrid sensor systems improve navigation accuracy, reduce localization errors, and enhance obstacle detection efficiency. The study concludes that hybrid sensor networks are essential for future autonomous robotic systems, with future research focusing on deep reinforcement learning, cloud robotics, edge computing, cognitive navigation, and IoRT-based architectures.
Hiroshi Tanaka, Yuki Nakamura· International Journal of Int...· 0 citations
The proposed approach improves fault detection accuracy, reduces downtime and maintenance costs, enhances robot reliability and productivity, and supports the development of self-aware robotic systems for Industry 4.0 and Industry 5.0 smart manufacturing environments.
Mahabala H. N.· International Journal of Int...· 0 citations
The paper provides the design and implementation of sensor-fusion-guided mobile robotic vehicle in efficient and safe industrial material handling. The suggested system is implemented with the help of an Arduino Uno microcontroller with several sensors such as ultrasonic sensors, infrared (IR) sensors and wheel encoders to provide efficient perception and tracking of the environment. The fusion of these heterogeneous sensors is achieved through sensor fusion so that the weaknesses may be overcome, which allows the sensors to detect obstacles reliably, guide paths and navigate successfully in dynamic industrial settings. Ultrasonic sensors offer a range of distance measurements that are long, whereas IR sensors offer a high accuracy of short-range measurements, and the wheel encoders facilitate odometry-based estimation of movement. An ESP8266/ESP32 IoT chip is also installed in the robotic vehicle, which enables real-time tracking, assigning tasks remotely and visualizing the status of the system with the help of a cloud interface. The driver allows motor control, which is the L293D driver, and brings smooth and precise movements of wheels to move through the predetermined industrial paths autonomously. The solution suggested can greatly decrease the number of people on the work, eliminate the risks of collisions, and enhance the efficiency of the working process on the warehouse floor and factory premises. The system combines affordable hardware with smart sensor fusion and IoT connectivity to provide a scalable and cost-effective and reliable solution to modern smart industry-wide material transportation.
V. Vijayabhasker, Beerala Venkatanaryana, Mahesh Mudavath· International journal of com...· 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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