Abstract
Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.
Highlights
Autonomous robots are smart machines capable of performing complex and repetitive tasks with a high level of independence from human intervention [1]
To reduce or eliminate the need for human involvement in monitoring challenging industrial environments and reduce the cost of large-scale internet of things (IoT) nodes deployment, we propose mobile IoT nodes carried onboard autonomous robots
We propose a thermal to visual frame-rate and resolution adaptive multi-modal image registration technique based on maximizing mutual information in the image gradient domain
Summary
Autonomous robots are smart machines capable of performing complex and repetitive tasks with a high level of independence from human intervention [1]. Recent advancements in AI and computer vision paved the way for robots to navigate challenging industrial environments for condition monitoring and assessment. They require less control and observation and can work efficiently and accurately [3]. Sensors 2020, 20, 6348 the reasons behind the challenging nature of industrial environments include the size of the monitored area, sensors cost, process effect on electronics and communications networks, obstacles, accessibility of monitored areas, high temperature, and low-lighting conditions. An example of challenging environments is industrial aluminum factories In these factories, steel pot shell surfaces’ temperature needs to be regularly measured to analyze sick pots, amperage increase, and design changes in pot lining.
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