Choice modeling of relook tasks for UAV search missions

Bertuccelli, Luca F.; Pellegrino, Nicholas; Cummings, M. L.

doi:10.1109/acc.2010.5530571

Cited by 21 publications

(12 citation statements)

References 14 publications

(19 reference statements)

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“…In this paper, it was assumed that all the tasks which the support system selected, will be serviced by the operator. Another possibility is to give operators ability to requeue tasks which has been shown to improve overall performance [2]. Developing stable attention allocation schemes with requeuing poses additional challenges and needs to be further investigated.…”

Section: Discussionmentioning

confidence: 99%

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Adaptive attention allocation in human-robot systems

Srivastava

Surana²,

Bullo

2012

2012 American Control Conference (ACC)

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Abstract-We propose an optimization framework to study two fundamental attention control aspects in human-robot systems: Where and how much attention should the operator allocate? In other words, which information source should be observed by the operator, and how much time duration should be allocated to the information feed in order to optimize the overall performance of the human-robot system? The proposed framework incorporates (i) operator performance constraints, such as error rates and service times based utilization history, (ii) sensor constraints, such as processing/travel time, and (iii) task constraints, such as prioritization. We use a receding horizon approach to solve the resulting dynamic program, leading to efficient policies for operator time duration allocation and sensor selection. We demonstrate our methodology in a distributed surveillance problem.

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Section: Discussionmentioning

confidence: 99%

“…Further, the optimal task release rate is also determined. Bertuccelli et al [2] study the queues with re-look tasks. They consider a queue in which the arriving difficult tasks are placed in an orbit queue for later viewing while simple tasks are immediately processed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive attention allocation in human-robot systems

Srivastava

Surana²,

Bullo

2012

2012 American Control Conference (ACC)

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“…A similar model has also been reported in the human factors literature, e.g., see [9]. Recently, there has also been interest in incorporating error rates into the performance metric for humans in a queueing setup, e.g., see [10], [11].…”

Section: Introductionmentioning

confidence: 87%

Dynamical queue-based task management policies for human operators

Savla

2011

Proceedings of the 2011 American Control Conference

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Abstract-Formal methods for task management for human operators are gathering increasing attention to improve efficiency of human-in-the-loop systems. In this paper, we consider a dynamical queue approach to task management for human operators. We consider the model of dynamical queue proposed in our earlier work [1], in which the service time depends on the server utilization history. The focus of the paper is to characterize the throughput of the dynamical queue and design corresponding maximally stabilizing task release control policies, assuming deterministic arrivals. We focus extensively on threshold policies that release a task to the server only when the server state is less than a certain threshold. When every task brings in the same deterministic amount of work, we give an exact characterization of the throughput and show that an appropriate threshold policy is maximally stabilizing. When the amount of work associated with the tasks is an i.i.d. random variable with finite support, we show that the maximum throughput increases in comparison to the case where the tasks have deterministic amount of work.

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“…b Preliminary investigation in the role of preemption has been addressed in a previous paper investigating use of relooks to improve the likelihood of detection [19]. Use of preemption for this work is investigated in an ongoing human-in-the-loop experiment.…”

Section: Iia Scheduling Formulationmentioning

confidence: 99%

“…Previous work has shown that search times can be modeled with a corresponding lognormal distribution [19]. A more detailed discussion on the sensitivity of the scheduling algorithms to the knowledge of search times in the next sections, but one method for handling the uncertainty in the start (respectively, search) times is to substitute the expected values,s j (respectively, t j ), and find the optimal schedule as follows: …”

Section: Iib Sensitivity To Uncertainty In Search Timesmentioning

confidence: 99%

Developing Operator Models for UAV Search Scheduling

Bertuccelli¹,

Beckers

Cummings³

2010

AIAA Guidance, Navigation, and Control Conference

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With the increased use of Unmanned Aerial Vehicles (UAVs), it is envisioned that UAV operators will become high level mission supervisors, responsible for information management and task planning. In the context of search missions, operators supervising a large number of UAVs can become overwhelmed with the sheer amount of information collected by the UAVs, making it difficult to optimize the information collection or direct their attention to the relevant data. Novel decisionsupport methods that account for realistic operator performance will therefore be required to aid the operators. This paper considers a decision support formulation for sequential search tasks, and discusses a non-preemptive scheduling formulation for a single operator performing a search mission in a time-constrained environment. The formulation is then generalized to include operator performance obtained from previous human-in-the-loop experiments, and presents one of the principal contributions of the paper. The sensitivity of the proposed model is analyzed in the presence of uncertainty to the operator model and search times, and a comparison is made between the expected performance difference between this scheduling system and a greedy scheduling strategy representative of operator planning. The paper concludes with the design of a human-in-the-loop experiment for a scheduling, replanning task for a simulated UAV mission.

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Choice modeling of relook tasks for UAV search missions

Cited by 21 publications

References 14 publications

Adaptive attention allocation in human-robot systems

Adaptive attention allocation in human-robot systems

Dynamical queue-based task management policies for human operators

Developing Operator Models for UAV Search Scheduling

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