Robust Task Scheduling for Multi-Operator Supervisory Control Missions

Peters, Jeffrey R.; Bertuccelli, Luca F.

doi:10.2514/1.i010444

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Further, cognitive under‐ and overloading of human operators is to be avoided by alternating demanding and less demanding tasks appropriately as well as by providing enough rest breaks. In the given time horizon, Peters and Bertuccelli schedule as many monitoring tasks to several operators as possible, given routes and speeds of drones, while penalizing situations of cognitive under‐ and overloading.…”

Section: Strategic Tactical and Operational Issues Linked To Drone mentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

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Section: Strategic Tactical and Operational Issues Linked To Drone mentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In particular, we consider the problem of scheduling tasks for human-UAV missions in the presence of uncertainty. Using the formulation detailed in [47], we apply our approach to a finite horizon scheduling problem. Our goal is to optimally schedule tasks in the presence of behavioral variance that accounts for levels of arousal or stress on cognitive performance [48].…”

Section: Application To Human-machine Optimizationmentioning

confidence: 99%

“…For example, the Yerkes-Dodson law is frequently used to capture the notion that intermediate levels of stress or arousal give rise to the best human performance [48]. This law can be imposed in the scheduling algorithm as a constraint that ensures that the task load of an unmanned aerial vehicle (UAV) operator is kept within predetermined bounds [47].…”

Section: Application To Human-machine Optimizationmentioning

confidence: 99%

“…The overall problem can posed as a linear program by relaxing the assumption that each task must fit into a single slot as done in [47]. Let x ij denote the fraction of task i that is performed in time (task) slot j (see Fig.…”

Section: Application To Human-machine Optimizationmentioning

confidence: 99%

“…2). Simplifying the formulation in [47], each task i can be specified by the following 3-tuple [t i , r i , δw i ]. Here t i is the amount of time it takes to complete task i, r i is the reward for completing the task, and δw i is the increase in task load of the operator due to the completion of the task.…”

Section: Application To Human-machine Optimizationmentioning

confidence: 99%

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