Towards an Ontology for BIM-Based Robotic Navigation and Inspection Tasks

Atrial fibrillation (AF) is linked to an increased risk of adverse cardiovascular events. While rhythm control with antiarrhythmic drugs (AADs) is a common strategy for managing patients with AF, catheter ablation may be a more efficacious and safer alternative to AADs for sinus rhythm control. Conventional catheter ablation has been associated with challenges during the arrhythmia mapping and ablation stages; however, the introduction of two remote catheter navigation systems (a robotic and a magnetic navigation system) may potentially overcome these challenges. Initial clinical experience with the robotic navigation system suggests that it offers similar procedural times, efficacy and safety to conventional manual ablation. Furthermore, it has been associated with reduced fluoroscopy exposure to the patient and the operator as well as a shorter fluoroscopy time compared with conventional catheter ablation. In the future, the remote navigation systems may become routinely used for complex catheter ablation procedures.

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Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Sensors ◽

10.3390/s21020642 ◽

2021 ◽

Vol 21 (2) ◽

pp. 642

Author(s):

Luis Miguel González de Santos ◽

Ernesto Frías Nores ◽

Joaquín Martínez Sánchez ◽

Higinio González Jorge

Keyword(s):

Path Planning ◽

Real Time ◽

Execution Time ◽

Point Cloud ◽

Indoor Environments ◽

Calculation Algorithm ◽

Time Restrictions ◽

Planning Algorithm ◽

Inspection Tasks ◽

Path Planning Algorithm

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

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Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs

Electronics ◽

10.3390/electronics10131511 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1511

Author(s):

Taylor Simons ◽

Dah-Jye Lee

Keyword(s):

Neural Networks ◽

Visual Inspection ◽

Deep Neural Networks ◽

Computational Cost ◽

Quality Inspection ◽

Agricultural Produce ◽

Resource Limited ◽

Inspection Tasks ◽

Computational Resources ◽

Small Models

There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target embedded visual inspection tasks, like quality inspection sorting on manufactured parts and agricultural produce sorting. We propose a new binarized convolutional layer, called the neural jet features layer, that learns well-known classic computer vision kernels that are efficient to calculate as a group. We show that on visual inspection tasks, neural jet features perform comparably to standard BNN convolutional layers while using less computational resources. We also show that neural jet features tend to be more stable than BNN convolution layers when training small models.

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Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection

Sensors ◽

10.3390/s21041385 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1385

Author(s):

Yurong Feng ◽

Kwaiwa Tse ◽

Shengyang Chen ◽

Chih-Yung Wen ◽

Boyang Li

Keyword(s):

System Design ◽

State Estimation ◽

Unmanned Aerial Vehicles ◽

Visual Inspection ◽

Spatial Information ◽

Free Flight ◽

Object State ◽

Aerial Vehicles ◽

Inspection Tasks ◽

Autonomous Inspection

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.

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Characterizing the Effects of Explosive Ordnance Disposal Operations on the Human Body While Wearing Heavy Personal Protective Equipment

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720821992623 ◽

2021 ◽

pp. 001872082199262

Author(s):

Yi-Ning Wu ◽

Adam Norton ◽

Michael R. Zielinski ◽

Pei-Chun Kao ◽

Andrew Stanwicks ◽

...

Keyword(s):

Human Body ◽

Personal Protective Equipment ◽

Evaluation Methodology ◽

Protective Equipment ◽

Physiological Measurement ◽

Cognitive Stress ◽

Explosive Ordnance Disposal ◽

And Performance ◽

Inspection Tasks ◽

Muscle Activations

Objective To provide a comprehensive characterization of explosive ordnance disposal (EOD) personal protective equipment (PPE) by evaluating its effects on the human body, specifically the poses, tasks, and conditions under which EOD operations are performed. Background EOD PPE is designed to protect technicians from a blast. The required features of protection make EOD PPE heavy, bulky, poorly ventilated, and difficult to maneuver in. It is not clear how the EOD PPE wearer physiologically adapts to maintain physical and cognitive performance during EOD operations. Method Fourteen participants performed EOD operations including mobility and inspection tasks with and without EOD PPE. Physiological measurement and kinematic data recording were used to record human physiological responses and performance. Results All physiological measures were significantly higher during the mobility and the inspection tasks when EOD PPE was worn. Participants spent significantly more time to complete the mobility tasks, whereas mixed results were found in the inspection tasks. Higher back muscle activations were seen in participants who performed object manipulation while wearing EOD PPE. Conclusion EOD operations while wearing EOD PPE pose significant physical stress on the human body. The wearer’s mobility is impacted by EOD PPE, resulting in decreased speed and higher muscle activations. Application The testing and evaluation methodology in this study can be used to benchmark future EOD PPE designs. Identifying hazards posed by EOD PPE lays the groundwork for developing mitigation plans, such as exoskeletons, to reduce physical and cognitive stress caused by EOD PPE on the wearers without compromising their operational performance.

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Robotic Navigation using Entropy-Based Exploration

2019 19th International Conference on Control, Automation and Systems (ICCAS) ◽

10.23919/iccas47443.2019.8971638 ◽

2019 ◽

Author(s):

Muhammad Usama ◽

Dong Eui Chang

Keyword(s):

Robotic Navigation

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Influence of Task and Situational Variables on the Applicability of Vigilance Data to Physical Security

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/1071181380024001156 ◽

1980 ◽

Vol 24 (1) ◽

pp. 606-607

Author(s):

Ben B. Morgan

Keyword(s):

Real World ◽

Human Performance ◽

Clear Trend ◽

General Belief ◽

Situational Variables ◽

Performance Requirements ◽

Limited Applicability ◽

Machine Systems ◽

Inspection Tasks ◽

The Many

Vigilance is one of the most thoroughly researched areas of human performance. Volumes have been written concerning vigilance performance in both laboratory and real-world settings, and there is a clear trend in the literature toward an increasing emphasis on the study of operational task behavior under environmental conditions that are common to real world jobs. Although a great deal of this research has been designed to test various aspects of the many theories of vigilance, there is a general belief that vigilance research is relevant and applicable to the performances required in real-world monitoring and inspection tasks. Indeed, many of the reported studies are justified on the basis of their apparent relevance to vigilance requirements in modern man-machine systems, industrial inspection tasks, and military jobs. There is a growing body of literature, however, which suggests that many vigilance studies are of limited applicability to operational task performance. For example, Kibler (1965) has argued that technological changes have altered job performance requirements to the extent that laboratory vigilance studies are no longer applicable to real-world jobs. Many others have simply been unable to reproduce the typical “vigilance decrement” in field situations. This has led Teichner (1974) to conclude that “the decremental function itself is more presumed than established.”

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