The mixed-initiative experimental testbed for collaborative human robot interactions

Barber, Daniel; Leontyev, S.; Sun, Bo; Davis, Larry S.; Nicholson, Denise; Chen, J.Y.C.

doi:10.1109/cts.2008.4543968

Cited by 31 publications

(20 citation statements)

References 3 publications

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“…The MIX Testbed is a distributed simulation environment for investigation into how robots are used and how automation affects human operator performance [19]. The Operator Control Unit (OCU) of the MIX Testbed ( Fig.…”

Section: Simulatormentioning

confidence: 99%

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Chen

Barnes

Kenny

2011

Proceedings of the 6th International Conference on Human-Robot Interaction

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A military multitasking environment was simulated to examine the effects of unreliable automation on the performance of robotics operators. The main task was to manage a team of four ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes based upon developments in the mission environment. RoboLeader's recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers. Results showed that the type of RoboLeader unreliability (falsealarm vs. miss prone) affected operator's performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density for multiple performance measures. Participants with higher spatial ability performed better on the two tasks that required the most visual scanning (target detection and route editing). Participants' attentional control impacted their overall multitasking performance, especially during their execution of the secondary tasks (communication and gauge monitoring).

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Section: Simulatormentioning

confidence: 99%

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Chen

Barnes

Kenny

2011

Proceedings of the 6th International Conference on Human-Robot Interaction

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“…Low-fidelity simulations, by contrast, may fail to capture the complexity and nuances of real multi-UV systems but provide a controlled tool for investigating a wide range of research issues. A wide variety of simulation platforms have been used in multi-UV HRI research, ranging from first-person shooter video games (Chadwick, Gillan, Simon, & Pazuchanics, 2004; Chen, 2010) to standalone multi-UAV simulations, such as UMAST (Narayanan et al, 2000), to sophisticated networked simulations, such as the Mixed Initiative Experimental Testbed, a distributed multi-UV simulation developed by the Army Research Laboratory (Barber, Davis, Nicholson, Finkelstein, & Chen, 2008). This section describes three widely used multi-UV simulations, including RoboFlag (see Figure 4.1), Research Environment for Supervisory Control of Heterogeneous Unmanned Vehicles (RESCHU), and USARSim, in order to provide a context for evaluating results and findings presented later in the chapter.…”

Section: Introductionmentioning

confidence: 99%

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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“…Simulator. The simulator for this study was a modified version of the Mixed-Initiative Experimental (MIX) Testbed (Barber et al, 2008). This distributed simulation environment was developed for investigating how automation affects human operator performance.…”

Section: Methodsmentioning

confidence: 99%

The Effects of Information Level on Human-Agent Interaction for Route Planning

Wright

Boyce

Chen

et al. 2015

Proceedings of the Human Factors and Ergonomics Society Annual

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Two experiments were conducted to examine the effects of level of information on human operators’ route selection decisions. Experiment 1 examined how information about resource usage/requirements affected route selection decisions for a remotely based supervisor guiding a dismounted soldier unit through an urban environment. Experiment 2 increased the level of information from Experiment 1 by adding a robotic asset to the unit and providing its resource usage/requirements. Decision time increased as the level of information increased and increased again with the addition of the robotic asset. In addition, as the level of information increased, preference for specific information sources began to vary. In the condition with the greatest level of information available, participants displayed no clear consensus as to preferred information source, with many indicating they relied upon sources that were unsuitable for successful mission completion. Future research could investigate further into the complexity of appropriate display for user interfaces involving robotic assets.

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The mixed-initiative experimental testbed for collaborative human robot interactions

Cited by 31 publications

References 3 publications

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Human Interaction With Multiple Remote Robots

The Effects of Information Level on Human-Agent Interaction for Route Planning

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