Identifying Suitable Algorithms for Human-Computer Collaborative Scheduling of Multiple Unmanned Vehicles

Clare, Andrew S.; Cummings, Mary L.; Bertuccelli, Luca F.; Hartford, E.

doi:10.2514/6.2012-485

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…This is only possible if appropriate scheduling algorithms are used that handle the uncertainty adequately. Many papers have dealt with this [282]- [285].…”

Section: A Enabling Technologies Of Industry 50 and Their Requirement...mentioning

confidence: 99%

Scheduling Under Uncertainty for Industry 4.0 and 5.0

et al. 2022

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This article provides a review about how uncertainties in increasingly complex production and supply chains should be addressed in scheduling tasks. Uncertainty management will be particularly important in Industry 5.0 solutions that will require the close integration of operators and technical systems. To prepare for these challenging developments, this work reviews the sources of uncertainty and the scheduling algorithms that deal with the different types of models developed to handle the uncertain nature of the elements and the environment of complex technological systems. The paper not only identifies the challenges, but also the main building blocks that can help to manage and reduce uncertainties based on the I4.0 and I5.0 solutions. We hope that this study will serve as a starting point for R&D projects and algorithm developments, which will be needed primarily in the field of multi-agent, multistage and inverse optimizations.

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“…This is only possible if appropriate scheduling algorithms are used that handle the uncertainty adequately. Many papers have dealt with this [282]- [285].…”

Section: A Enabling Technologies Of Industry 50 and Their Requirement...mentioning

confidence: 99%

Scheduling Under Uncertainty for Industry 4.0 and 5.0

et al. 2022

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“…To demonstrate how much better a system can perform when designed to promote collaborative human-automated planning as depicted in Figure 1, several examples from the domain of Unmanned Vehicles will be used (Clare, et al 2012;Cummings et al, 2012). Temporal influence will also be discussed, with a focus on both high and low task loading.…”

Section: Figure 1: Decision Making Allocation Between a Human Operatomentioning

confidence: 99%

Discussion Panel: Collaborative Automation Across Varying Time Scales of Interaction: What’s the Same? What’s Different?

Roth¹,

Cummings

Miller

et al. 2012

Proceedings of the Human Factors and Ergonomics Society Annual

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Automated systems, ranging from robots to intelligent planning aids, are increasingly part of the cognitive landscape. A pressing question facing the human factors community is how to design the automation to foster effective collaboration between the human and automated agents. This panel brings together leading researchers actively engaged in design of decision-support systems that involve some element of 'cognitive work' automation. They represent a variety of domains and a variety of approaches to design of collaborative automation. Panel members will discuss unique challenges of their respective domains, including differences in temporal rhythm, and how these have shaped their perspective on 'collaborative aiding'.

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Adaptive Fuzzy Collaborative Task Assignment for Heterogeneous Multirobot Systems

Zhang

Zhong

Nof

2015

Int. J. Intell. Syst.

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Heterogeneity in robot model mix is advantageous in emerging and future robotic applications. For instance, in a fully automated industrial package loading and unloading scenario, a variety uncertain types of packages need to be continuously sorted and loaded onto designated trucks. To handle the variation of tasks, multiple types of robots, or heterogeneous robots, are designed in the system. However, in such a collaborative system consisting of heterogeneous robots, ineffective task assignments often lead to bad collaboration and thus poor efficiency. To improve the robot collaboration, we herein define the collaborative task assignment problem and develop a fuzzy collaborative intelligence based algorithm to optimize the assignment plans. Specifically, the collaboration type, the collaboration matrix, and the assignment matrix are defined and the new algorithm for adaptive fuzzy collaborative task assignment relies on intuitionistic fuzzy set theory. An unsorted package loading and unloading tasks by heterogeneous robots is used as a case example to validate the new algorithm. Experiments indicate with statistical significance that the new approach shortens total completion time by 21%, reduces total energy consumption by 23%, and increases loading accuracy by 31%, compared with the traditional static task assignment method commonly practiced. The developed approach can be applied to different emerging collaborative systems to improve systems’ collaborative intelligence.

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Identifying Suitable Algorithms for Human-Computer Collaborative Scheduling of Multiple Unmanned Vehicles

Cited by 4 publications

References 35 publications

Scheduling Under Uncertainty for Industry 4.0 and 5.0

Scheduling Under Uncertainty for Industry 4.0 and 5.0

Discussion Panel: Collaborative Automation Across Varying Time Scales of Interaction: What’s the Same? What’s Different?

Adaptive Fuzzy Collaborative Task Assignment for Heterogeneous Multirobot Systems

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