Safe Cooperation between Human Operators and Visually Controlled Industrial Manipulators

FUZZY COGNITIVE MODELS OF HUMAN OPERATORS AND DISPATCHERS DECISION SUPPORT

ELECTRICAL AND COMPUTER SYSTEMS ◽

10.15276/etks.13.89.2014.23 ◽

2014 ◽

pp. 160-169

Author(s):

N. Kopytchuk ◽

◽

V. Peredery ◽

A. Eremenko

Keyword(s):

Decision Support ◽

Cognitive Models ◽

Human Operators

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽

10.1609/aimag.v40i3.4810 ◽

2019 ◽

Vol 40 (3) ◽

pp. 41-57

Author(s):

Manisha Mishra ◽

Pujitha Mannaru ◽

David Sidoti ◽

Adam Bienkowski ◽

Lingyi Zhang ◽

...

Keyword(s):

Decision Support ◽

Situational Awareness ◽

Feature Space ◽

Human Interaction ◽

Superior Performance ◽

Uncertain Environments ◽

Cognitive States ◽

Contextual Elements ◽

Human Operators ◽

Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances autonomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

An Optically Driven Learning Control for Industrial Manipulators

10.23919/acc.1988.4789791 ◽

1988 ◽

Cited By ~ 2

Author(s):

R. Shoureshi ◽

P. Brown ◽

R. Evans ◽

W. Stevenson

Keyword(s):

Learning Control ◽

Industrial Manipulators

The Pilotless Air Force: A Look at Replacing Human Operators with Advanced Technology

10.21236/ada397852 ◽

1997 ◽

Cited By ~ 3

Author(s):

Robert C. Nolan ◽

II

Keyword(s):

Air Force ◽

Advanced Technology ◽

Human Operators

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽

10.3390/s21144805 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4805

Author(s):

Saad Abbasi ◽

Mahmoud Famouri ◽

Mohammad Javad Shafiee ◽

Alexander Wong

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Detection Methods ◽

Detection Accuracy ◽

Network Architectures ◽

Design Exploration ◽

Convolutional Autoencoder ◽

Acoustic Anomaly ◽

Human Operators ◽

Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641310 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1302-1307

Author(s):

Richard Stone ◽

Minglu Wang ◽

Thomas Schnieders ◽

Esraa Abdelall

Keyword(s):

Visual Information ◽

Human Perception ◽

Low Level ◽

Hit Rate ◽

Word Level ◽

Level Of Automation ◽

Significant Difference ◽

Human Operators ◽

And Performance ◽

High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

Ears in the Sky: Potential of Drones for the Bioacoustic Monitoring of Birds and Bats

Drones ◽

10.3390/drones5010009 ◽

2021 ◽

Vol 5 (1) ◽

pp. 9

Author(s):

Adrien Michez ◽

Stéphane Broset ◽

Philippe Lejeune

Keyword(s):

Forest Canopy ◽

Low Cost ◽

Population Monitoring ◽

Biodiversity Loss ◽

Active Species ◽

Terrestrial Mammals ◽

Technical Developments ◽

High Resolution Imagery ◽

Human Operators ◽

The Impact

In the context of global biodiversity loss, wildlife population monitoring is a major challenge. Some innovative techniques such as the use of drones—also called unmanned aerial vehicle/system (UAV/UAS)—offer promising opportunities. The potential of UAS-based wildlife census using high-resolution imagery is now well established for terrestrial mammals or birds that can be seen on images. Nevertheless, the ability of UASs to detect non-conspicuous species, such as small birds below the forest canopy, remains an open question. This issue can be solved with bioacoustics for acoustically active species such as bats and birds. In this context, UASs represent an interesting solution that could be deployed on a larger scale, at lower risk for the operator, and over hard-to-reach locations, such as forest canopies or complex topographies, when compared with traditional protocols (fixed location recorders placed or handled by human operators). In this context, this study proposes a methodological framework to assess the potential of UASs in bioacoustic surveys for birds and bats, using low-cost audible and ultrasound recorders mounted on a low-cost quadcopter UAS (DJI Phantom 3 Pro). The proposed methodological workflow can be straightforwardly replicated in other contexts to test the impact of other UAS bioacoustic recording platforms in relation to the targeted species and the specific UAS design. This protocol allows one to evaluate the sensitivity of UAS approaches through the estimate of the effective detection radius for the different species investigated at several flight heights. The results of this study suggest a strong potential for the bioacoustic monitoring of birds but are more contrasted for bat recordings, mainly due to quadcopter noise (i.e., electronic speed controller (ESC) noise) but also, in a certain manner, to the experimental design (use of a directional speaker with limited call intensity). Technical developments, such as the use of a winch to safely extent the distance between the UAS and the recorder during UAS sound recordings or the development of an innovative platform, such as a plane–blimp hybrid UAS, should make it possible to solve these issues.

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Electronics ◽

10.3390/electronics10111317 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1317

Author(s):

Alejandro Chacón ◽

Pere Ponsa ◽

Cecilio Angulo

Keyword(s):

Interaction Analysis ◽

Mental Workload ◽

Cognitive Task ◽

Physical Work ◽

Work Skills ◽

Dexterous Manipulation ◽

Assembly Task ◽

Cognitive Interaction ◽

Human Operators ◽

Assembly Tasks

In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

On the Influence of Human Factors on Safety of Remotely-Controlled Merchant Vessels

Applied Sciences ◽

10.3390/app11031145 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1145

Author(s):

Krzysztof Wróbel ◽

Mateusz Gil ◽

Chong-Ju Chae

Keyword(s):

Human Factors ◽

Human Factor ◽

Potential Influence ◽

General Belief ◽

Causal Factors ◽

Physiological Conditions ◽

Factors Analysis ◽

Important Barrier ◽

Human Operators ◽

Expert Study

With numerous efforts undertaken by both industry and academia to develop and implement autonomous merchant vessels, their safety remains an utmost priority. One of the modes of their operation which is expected to be used is a remote control. Therein, some, if not all, decisions will be made remotely by human operators and executed locally by a vessel control system. This arrangement incorporates a possibility of a human factor occurrence. To this end, a variety of factors are known in the literature along with a complex network of mutual relationships between them. In order to study their potential influence on the safety of remotely-controlled merchant vessels, an expert study has been conducted using the Human Factors Analysis and Classification System-Maritime Accidents (HFACS–MA) framework. The results indicate that the most relevant for the safety of this prospective system is to ensure that known problems are properly and timely rectified and that remote operators maintain their psycho- and physiological conditions. The experts elicited have also assigned higher significance to the causal factors of active failures than latent failures, thus indicating a general belief that operators’ actions represent the final and the most important barrier against accident occurrence.

Comparing Two Decision Support Modes Using the Cognitive Shadow Online Policy-Capturing System

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641270 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1125-1129

Author(s):

Katherine Labonté ◽

Daniel Lafond ◽

Aren Hunter ◽

Heather F. Neyedli ◽

Sébastien Tremblay

Keyword(s):

Decision Making ◽

Decision Support ◽

Response Pattern ◽

Policy Capturing ◽

Model Accuracy ◽

Prototype Tool ◽

Human Operators ◽

Support Decision Making ◽

Threat Evaluation

The Cognitive Shadow is a prototype tool intended to support decision making by autonomously modeling human operators’ response pattern and providing online notifications to the operators about the decision they are expected to make in new situations. Since the system can be configured either in a reactive “shadowing” or a proactive “recommendation” mode, this study aimed to determine its most effective mode in terms of human and model accuracy, workload, and trust. Subjects participated in an aircraft threat evaluation simulation without decision support or while using either mode of the Cognitive Shadow. Whereas the recommendation mode had no advantage over the control condition, the shadowing mode led to higher human and model accuracy. These benefits were maintained even when the tool was unexpectedly removed. Neither mode influenced workload, and the initial lower trust rating in the shadowing mode faded quickly, making it the best overall configuration for the cognitive assistant.