The Impact of Human–Automation Collaboration in Decentralized Multiple Unmanned Vehicle Control

Cummings, Mary L.; How, Jonathan P.; Whitten, Andrew K.; Toupet, Olivier

doi:10.1109/jproc.2011.2174104

Cited by 69 publications

(50 citation statements)

References 19 publications

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“…As autonomy capabilities improve, there is increasing interest in giving individual operators responsibility for multiple autonomous vehicles. Because of varying demands for each UAV, sometimes a lower number of UAVs will cause more work and we cannot measure operator workload based solely on number of vehicles [12]. A successful dynamic difficulty system would better optimize distribution of workload and the utilization of human resources to maximize performance.…”

Section: Application: Uav Path Planningmentioning

confidence: 99%

Dynamic difficulty using brain metrics of workload

Afergan

Peck

Solovey

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

117

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Dynamic difficulty adjustments can be used in humancomputer systems in order to improve user engagement and performance. In this paper, we use functional near-infrared spectroscopy (fNIRS) to obtain passive brain sensing data and detect extended periods of boredom or overload. From these physiological signals, we can adapt a simulation in order to optimize workload in real-time, which allows the system to better fit the task to the user from moment to moment. To demonstrate this idea, we ran a laboratory study in which participants performed path planning for multiple unmanned aerial vehicles (UAVs) in a simulation. Based on their state, we varied the difficulty of the task by adding or removing UAVs and found that we were able to decrease errors by 35% over a baseline condition. Our results show that we can use fNIRS brain sensing to detect task difficulty in real-time and construct an interface that improves user performance through dynamic difficulty adjustment.

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Section: Application: Uav Path Planningmentioning

confidence: 99%

Dynamic difficulty using brain metrics of workload

Afergan

Peck

Solovey

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

117

View full text Add to dashboard Cite

show abstract

“…This thesis chapter includes work which was presented as a co-authored research paper [54,120,121]. Andrew Clare, Olivier Toupet, Jonathan P. How, and Mary L. Cummings are acknowledged for their contributions to this paper as well as the technical work described in this chapter.…”

Section: Motivation and Overviewmentioning

confidence: 99%

Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams

et al. 2013

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A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution.

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“…In the second condition, humans + automation, humans were allowed to update the algorithm's tasking or replan more often if they thought the automation was not performing adequately. The details of this experiment are provided in [15]. Figure 2 demonstrates just how much value added the human provided in terms of the two primary dependent measures, percentage of area covered and number of targets found.…”

Section: The Need For Human Oversight Of Automated Planningmentioning

confidence: 99%

Paying Attention to the Man behind the Curtain

Cummings

Thornburg

2011

IEEE Pervasive Comput.

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Keywords: human supervisory control, decentralized systems, human-computer interface, smart grid, decision support, power plantsIn the push to develop smart energy systems, there is increasing focus on how to design systems that measure and predict user behavior in order to effect optimal energy consumption. While such focus is clearly an important component in the success of these future systems, substantially less attention is paid to the human on the other side of the energy system loop -the supervisors of power generation processes, the proverbial men (or women) behind the curtain. Out of sight and sadly in terms of technological advancements, out of mind, today these operators perform high risk jobs in often datarich, but information-impoverished settings. For these operators, pervasive computing of the future will likely add to an already complex array of data streams, and introduce a new layer of supervisory complexity in response to the goal of dynamically adapting energy management.The Three Mile Island nuclear power plant accident in 1979 was caused primarily by operator misunderstanding of sensor data from an overwhelmingly complex control panel [1]. More recently in 2003 in the Northeast, operators were not able to both see and understand critical system states for nearby power grids, ultimately leading to the largest blackout in North American history which contributed to at least 11 deaths and cost an estimated $6 billion [2]. In these high profile cases, and in countless other more minor electric and nuclear power plant incidents, a significant problem was and continues to be the lack of explicit design to support rapid data aggregation and information visualization to support supervisors' time-pressured decision making.The development of smart energy systems that leverage pervasive computing could further add to the workload of these supervisory control operators who will have to predict possible power plant load and production changes due to environmental and plant events, as well as dynamic system adaptation in response to customer behaviors. Contrary to many assumptions, the insertion of more automation, both in terms of distributed sensors and algorithms to post-process data for operators, will not necessary reduce workload, nor necessarily improve system performance. These concepts are explored in more detail in the following sections. Supervisory Control and Workload in Power GenerationCurrent power generation operations are highly automated. In normal, day-to-day operations, automation controls the adjustment of system parameters, while human operators generally take the role of system supervisors, monitoring system states and typically intervening in only non-monitoring operations, such as responding to an alarm, managing a plant start-up, or overseeing other off-nominal operations. However, in present-day power generation operations, while the system itself is highly automated, little automation is used to support and augment supervisor decision-making and performance, especially in time...

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The Impact of Human–Automation Collaboration in Decentralized Multiple Unmanned Vehicle Control

Cited by 69 publications

References 19 publications

Dynamic difficulty using brain metrics of workload

Dynamic difficulty using brain metrics of workload

Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams

Paying Attention to the Man behind the Curtain

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