Human in the loop evaluation of a mixed-initiative system for planning and control of multiple UAV teams

Roth, Emilie M.; Hanson, Mark L.; Hopkins, Chris; Mancuso, Vince; Zacharias, Greg L.

doi:10.1037/e577272012-001

Cited by 9 publications

(11 citation statements)

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“…Similar findings were obtained by Roth, Hanson, Hopkins, Mancuso, and Zacharias (2004) in evaluating the extent to which operators of unmanned aerial vehicles (UAVs) understood the plans developed by automated controllers. Humans and automatic agents cooperated as a team in planning and executing missions.…”

Section: Intermediate-and Low-levels Of Automation and Operator's Persupporting

confidence: 66%

Human-system Interfaces to Automatic Systems: Review Guidance and Technical Basis

O’Hara¹,

Higgins²

2010

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Automation is ubiquitous in modern complex systems, and commercial nuclear-power plants are no exception. Beyond controlling the plant's functions and systems, automation is applied to a wide range of other functions, including monitoring and detection, situation assessment, response planning, response implementation, and interface management. Automation has become a "team player" supporting personnel in nearly all aspects of operating the plant. In light of its increasing use and importance in new-and future-plants, guidance is needed to enable the NRC's staff to conduct safety reviews of the human factors engineering (HFE) aspects of modern automation. The objective of our research that we describe in this report was to develop guidance for reviewing the operator's interface with automation. We first characterized the important HFE aspects of automation, based on how it is implemented in current systems. This description had six dimensions: Levels of automation, functions of automation, processes of automation, modes of automation, flexibility of allocation, and reliability of automation. Next, we reviewed literature on the effects of all of these aspects of automation on human performance, and on the design of human-system interfaces (HSIs). Then, we used this technical basis established from the literature to develop guidance for reviewing designs. It consists of the following seven topics: Automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and, HSI integration. In addition, we offer our insights into the automationdesign process, operator training, and operations. v ACKNOWLEDGMENTS This research is sponsored by the U.S. Nuclear Regulatory Commission. The views presented represent those of the authors alone and not necessarily those of the NRC. The authors wish to thank Stephen Fleger, the NRC Project Manager, and former Project Managers, Mike Boggie and Niav Hughes for their guidance and input during this research project. We are grateful to Val Barnes for her insights and helpful comments on the report. We also thank Avril Woodhead for her technical editing of this report and Maryann Julian for preparing the manuscript.

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Section: Intermediate-and Low-levels Of Automation and Operator's Persupporting

confidence: 66%

Human-system Interfaces to Automatic Systems: Review Guidance and Technical Basis

O’Hara¹,

Higgins²

2010

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“…The operator chooses one of these plans and then returns to the map display. These interactions incorporate two features missing in the Roth et al (2004) system. The designation of new search tasks is made directly on the domain of interest, the map, rather than indirectly through manipulating the objective function.…”

Section: Cooperative Plannersmentioning

confidence: 99%

“…• Cooperative planners are appropriate when UV behaviors are relatively independent and a relatively simple objective functions can characterize operator intent. Interaction methods that allow an operator to express intent in the domain of interest are preferred (Cummings et al, 2012;Roth et al, 2004). • Plan libraries are appropriate when UV behaviors are highly interdependent (Endo et al, 2004;Miller & Parasuraman, 2007).…”

Section: O(n) Tasksmentioning

confidence: 99%

“…Interactions with planners, therefore, may involve a test loop in which the operator proposes changes and then views test results before implementation (“what-if” testing). Roth, Hanson, Hopkins, Mancuso, and Zacharias (2004) and Hanson, Roth, Hopkins, and Mancuso (2004), for example, report participants’ difficulties in commanding multiple-UAV teams using changes to “intent matrices” that altered relative target values in their planner’s objective function. Their operators expressed confusion over the ordering and choices of targets in the resulting plans and had difficulties in adjusting entries to achieve their desired result.…”

Section: Interacting With Planners: O(1)mentioning

confidence: 99%

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Human Interaction With Multiple Remote Robots

Lewis¹

2013

Reviews of Human Factors and Ergonomics

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In this chapter, I review research involving remote human supervision of multiple unmanned vehicles (UVs) using command complexity as an organizing construct. Multi-UV tasks range from foraging, requiring little coordination among UVs, to formation following, in which UVs must function as a cohesive unit. Command complexity, the degree to which operator effort increases with the number of supervised UVs, is used to categorize human interaction with multiple UVs. For systems in which each UV requires the same form of attention (O(n)), effort increases linearly with the number of UVs. For systems in which the control of one UV is dependent upon another (O(>n)), additional UVs impose greater than linear increases due to the expense of coordination. For other systems, an operator interacts with an autonomously coordinating group, and effort is unaffected by group size (O(1)). Studies of human/multi-UV interaction can be roughly grouped into O(n) supervision, involving one-to-one control of individual UVs, or O(1) commanding, in which higherlevel commands are directed to a group. Research in O(n) command has centered on roundrobin control, neglect tolerance, and attention switching. Approaches to O(1) command are divided into systems using autonomous path planning only, plan libraries, human-steered planners, and swarms. Each type of system has its advantages. Less complete work in scalable displays for multiple UVs is reviewed. Mixing levels of command is probably necessary to supervise multiple UVs performing realistic tasks. Research in O(n) control is mature and can provide quantitative and qualitative guidance for design. Interaction with planners and swarms is less mature but more critical to developing effective multi-UV systems capable of performing complex tasks. IntRoductIon In recent decades, the number of mobile unmanned vehicles (UVs) deployed in field applications has risen dramatically. Their usage offers obvious advantages of reduced costs, removing humans from harm's way, or enabling entirely new applications that were previously impossible, especially when combining many such UVs into a comprehensive system. The domains of potential application are equally diverse and range from low-cost warehouse security to interplanetary exploration. New developments in commodity hardware, which serve as low-cost replacements for otherwise expensive sensing or motion capabilities, promise to further accelerate the trend toward deploying large teams of mobile UVs. This trend, however, poses a challenge for the control of such systems.

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“…component-specific calibration). Roth et al, (2004) also reported that, in order to enhance the mixed-initiative team performance, the automated system should communicate to the human operator about its rationale for the systemgenerated plans while allowing the operator flexibility in comparing different alternatives and testing "what-if" scenarios. While excessive alerts may create the cry-wolf effect, Lees and Lee (2007) did find that "unnecessary alarms" (i.e., alerts that are legitimate but, due to the peculiarities of the situation, do not require operator compliance) foster the operators' trust in the automated system and actually enhance their compliance with the alerts rather than reducing it.…”

Section: Implications For System Design-trust Calibrationmentioning

confidence: 99%

Human-Agent Teaming for Multi-Robot Control: A Literature Review

Jessie¹,

Barnes²

2013

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The literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots; appropriate human trust in the automated systems; maintenance of human operator's situation awareness; individual differences in human-agent (H-A) interaction; and retention of human decision authority. A number of approaches, from flexible automation to autonomous agents, were reviewed and their advantages and disadvantages were discussed. Also discussed were two key human performance issues (trust and situation awareness) related to H-A teaming for multi-robot control and some promising user interface design solutions to address these issues. Some key individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.

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Human in the loop evaluation of a mixed-initiative system for planning and control of multiple UAV teams

Cited by 9 publications

References 0 publications

Human-system Interfaces to Automatic Systems: Review Guidance and Technical Basis

Human-system Interfaces to Automatic Systems: Review Guidance and Technical Basis

Human Interaction With Multiple Remote Robots

Human-Agent Teaming for Multi-Robot Control: A Literature Review

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