Informing System Design Using Human Performance Modeling

Watson, Michael; Rusnock, Christina F.; Miller, Michael E.; Colombi, John M.

doi:10.1002/sys.21388

Cited by 22 publications

(8 citation statements)

References 34 publications

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“…2) Human Performance Model: We model the human operator as an event sever responsible for handling image classification tasks generated by both UAVs and UGVs. Based on the empirical findings regarding human performance in complex tasks [26], we assume that the human server handles events serially. The performance of the binary threat identification task is assumed to be affected by the fatigue and workload of the human operator.…”

Section: B Simulation Environmentmentioning

confidence: 99%

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Wang¹,

Zhao²,

Min³

2023

Preprint

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Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise.To efficiently operate such highly heterogeneous teams and maximize team performance timely, sophisticated initial task allocation strategies that consider individual differences across team members and tasks are required. While existing works have shown promising results in reallocating tasks based on agent state and performance, the neglect of the inherent heterogeneity of the team hinders their effectiveness in realistic scenarios. In this paper, we present a novel formulation of the initial task allocation problem in multi-human multirobot teams as contextual multi-attribute decision-make process and propose an attention-based deep reinforcement learning approach. We introduce a cross-attribute attention module to encode the latent and complex dependencies of multiple attributes in the state representation. We conduct a case study in a massive threat surveillance scenario and demonstrate the strengths of our model.

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Section: B Simulation Environmentmentioning

confidence: 99%

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Wang¹,

Zhao²,

Min³

2023

Preprint

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“…Similar studies on simulated human-UAV teams have investigated the performance of mixed asset types 29,30 and the impact on speed and accuracy of various human-automation workflows. 31…”

Section: Simulated Human Behavior In Human-agent Teamsmentioning

confidence: 99%

“…The Fusion and IMPACT projects 1,28 are an attempt to create agile teams of humans and multiple UAVs through advanced user interfaces, sports‐inspired command schemes, cognitive modeling plugins, and artificial intelligence. Similar studies on simulated human‐UAV teams have investigated the performance of mixed asset types 29,30 and the impact on speed and accuracy of various human‐automation workflows 31 …”

Section: Related Workmentioning

confidence: 99%

Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

Humann

Fletcher

Gerdes

2023

Systems Engineering

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As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.

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“…SysML was chosen to represent aspects of human digital twins as the digital twin concept has been described as a method to extend Model-Based Systems Engineering (MBSE) from the design to the operation and sustainment phase of a product (Boschert and Rosen 2016) and SysML has been developed to support MBSE. Furthermore, this language is an object-based language which permits the description of a generic system, sometimes through a reference architecture, and the extension of this description to specific instances or instantiations (King et al 2020) and has been used to model the human's contribution to system performance (M. Watson et al 2017a, b; M. E. Watson et al 2017a, b). Thus, the generic system description provided through the diagrams in this paper can be extended to illustrate the application of human digital twins in specific applications or instances.…”

Section: Modeling Structure and Use Casesmentioning

confidence: 99%

A unified view of a human digital twin

Miller

Spatz

2022

Hum.-Intell. Syst. Integr.

Self Cite

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The term human digital twin has recently been applied in many domains, including medical and manufacturing. This term extends the digital twin concept, which has been illustrated to provide enhanced system performance as it combines system models and analyses with real-time measurements for an individual system to improve system maintenance. Human digital twins have the potential to change the practice of human system integration as these systems employ real-time sensing and feedback to tightly couple measurements of human performance, behavior, and environmental influences throughout a product’s life cycle to human models to improve system design and performance. However, as this concept is relatively new, the literature lacks inclusive and precise definitions of this concept. The current research reviews the literature on human digital twins to provide a generalized structure of these systems, provide definitions of a human digital twin and human digital twin system, and review the potential applications of these systems within product design, development, and sustainment. This review of the existing literature suggests that components of human models sufficient to provide robust human digital twins are likely to be derived across multiple fields of study. Thus, development of these systems would benefit an open multi-disciplinary research effort.

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Informing System Design Using Human Performance Modeling

Cited by 22 publications

References 34 publications

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

A unified view of a human digital twin

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