Dynamic Operator Overload: A Model for Predicting Workload During Supervisory Control

Breslow, Leonard A.; Gartenberg, Daniel; McCurry, J. Malcolm; Trafton, J. Gregory

doi:10.1109/tsmc.2013.2293317

Cited by 22 publications

(10 citation statements)

References 15 publications

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“…1, top left) is used by the operator to perform a manual visual acquisition task once the UAV has reached the target. It is not critical to this work so we largely omit its consideration; it is more fully described in Breslow, Gartenberg, McCurry, and Trafton (2014).…”

Section: Task Descriptionmentioning

confidence: 99%

Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models

Hiatt

Brooks

Trafton

2022

Topics in Cognitive Science

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We describe a new approach for developing and validating cognitive process models. In our methodology, graphical models (specifically, hidden Markov models) are developed both from human empirical data on a task and synthetic data traces generated by a cognitive process model of human behavior on the task. Differences between the two graphical models can then be used to drive model refinement. We show that iteratively using this methodology can unveil substantive and nuanced imperfections of cognitive process models that can then be addressed to increase their fidelity to empirical data.

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Section: Task Descriptionmentioning

confidence: 99%

Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models

Hiatt

Brooks

Trafton

2022

Topics in Cognitive Science

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“…Attention is a cognitive function and thus is difficult to measure directly. Eye tracking technology enables physiological measurements linked to various aspects of human cognition, including attention [77][78][79][80][81]. The role of eye movements and visual attention in reading, scene perception, visual search, and other information processing has long been studied [82][83][84][85].…”

Section: Eye Tracking For Human-robot Interactionmentioning

confidence: 99%

“…The number of fixations on an object represented the allocation of attention. Fixation has been applied as a measure of attention allocation for an online predictive model of operator overload during supervisory control of multiple unmanned aerial vehicles (UAVs) within a simulation environment [81]. A logistic regression model, developed to predict vehicle damage when an operator failed to correct a collision course, was applied to generate real-time alerts.…”

Section: Eye Tracking For Human-robot Interactionmentioning

confidence: 99%

Multirobot Confidence and Behavior Modeling: An Evaluation of Semiautonomous Task Performance and Efficiency

Lucas

Pandya

2021

Robotics

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There is considerable interest in multirobot systems capable of performing spatially distributed, hazardous, and complex tasks as a team leveraging the unique abilities of humans and automated machines working alongside each other. The limitations of human perception and cognition affect operators’ ability to integrate information from multiple mobile robots, switch between their spatial frames of reference, and divide attention among many sensory inputs and command outputs. Automation is necessary to help the operator manage increasing demands as the number of robots (and humans) scales up. However, more automation does not necessarily equate to better performance. A generalized robot confidence model was developed, which transforms key operator attention indicators to a robot confidence value for each robot to enable the robots’ adaptive behaviors. This model was implemented in a multirobot test platform with the operator commanding robot trajectories using a computer mouse and an eye tracker providing gaze data used to estimate dynamic operator attention. The human-attention-based robot confidence model dynamically adapted the behavior of individual robots in response to operator attention. The model was successfully evaluated to reveal evidence linking average robot confidence to multirobot search task performance and efficiency. The contributions of this work provide essential steps toward effective human operation of multiple unmanned vehicles to perform spatially distributed and hazardous tasks in complex environments for space exploration, defense, homeland security, search and rescue, and other real-world applications.

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“…Breslow et al 61 extended upon this model by considering the possibility of a highly dynamic mission environment. They postulated that instances where multiple robots will simultaneously require the human's attention are likely.…”

Section: Model-based Approaches To Automated Evaluation Of Human-robot Systemsmentioning

confidence: 99%

Workload, awareness and automation in multiple-robot supervision

Wong

Seet

2017

International Journal of Advanced Robotic Systems

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Using a single human to supervise multiple robots helps to address manpower constraints while deriving the benefits of multiple-robot deployment such as efficiency and improved system reliability. However, it can also induce high supervisor workload and diminish situation awareness. This article explains workload and situation awareness. It reviews various studies related to human-robot systems to illustrate the effects and causes of workload and diminished situation awareness in such systems. The article reviews and discusses the application of automation to address workload and situation-awareness concerns. It also presents the issues that the use of automation can cause, highlighting that automation must be applied with care. The article advocates the consideration of sliding autonomy for four aspects of task execution: information acquisition, information analysis, decision selection and action implementation. It additionally encourages the appreciation for recognized methods of applying and triggering automation. The hope is for robots to be equipped with adjustable autonomy across multiple aspects of task performance to create robotic systems with highly flexible autonomy configurations. While robots from such systems may have the flexibility to deal with numerous situation requirements, the research challenge is understanding if and how such flexibility will affect human workload.

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Dynamic Operator Overload: A Model for Predicting Workload During Supervisory Control

Cited by 22 publications

References 15 publications

Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models

Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models

Multirobot Confidence and Behavior Modeling: An Evaluation of Semiautonomous Task Performance and Efficiency

Workload, awareness and automation in multiple-robot supervision

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