Virtual, augmented and mixed reality.

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Individual Differences In Human-Robot Interaction In A Military Multitasking Environment

A military vehicle crew station environment was simulated and a series of three experiments was conducted to examine the workload and performance of the combined position of the gunner and robotics operator in a multitasking environment. The study also evaluated whether aided target recognition (AiTR) capabilities (delivered through tactile and/or visual cuing) for the gunnery task might benefit the concurrent robotics and communication tasks and how the concurrent task performance might be affected when the AiTR was unreliable (i.e., false alarm prone or miss prone). Participants’ spatial ability was consistently found to be a reliable predictor of their targeting task performance as well as their modality preference for the AiTR display. Participants’ attentional control was found to significantly affect the way they interacted with unreliable automated systems. © 2011, Human Factors and Ergonomics Society. All rights reserved

Individual Differences In Concurrent Performance Of Military And Robotics Tasks With Tactile Cueing

We simulated a military tank environment and examined the performance of the combined position of gunner and robotics operator and how aided target recognition (AiTR) capabilities (delivered either through tactile or tactile + visual cueing) for the gunnery task might benefit the concurrent robotics and communication tasks. Specifically, we investigated whether performance was affected by individual differences factors such as spatial ability and perceived attentional control. Results showed that participants\u27 robotics and communication tasks both improved significantly when the AiTR was available to assist them with their gunnery task. The participants\u27 spatial ability was found to be a good indicator of their gunnery and robotics task performance. However, when AiTR was available to assist their gunnery task, those participants of lower spatial ability were able to perform their robotics tasks as well as those of higher spatial ability. There was also evidence that operators\u27 preference of cueing modality was related to their spatial ability and attentional control

Uav-Guided Navigation For Ground Robot Operations

We simulated a military reconnaissance environment and examined the performance of ground robotics operators who needed to utilize sensor images from an unmanned aerial vehicle (UAV) to navigate his/her ground robot to the locations of the targets. We also evaluated participants\u27 spatial ability and examined if it affected their performance or perceived workload. Results showed that participants\u27 overall performance (speed and accuracy) was better when s/he had access to images from larger UAVs with fixed orientations, compared to other UAV conditions (baseline- no UAV, micro air vehicle, and UAV with orbiting views). Participants experienced the highest workload when the UAV was orbiting

To Warn Or Not To Warn

Increased automation has brought about the issue of having too many alarms in a variety of human-machine systems such as aviation, nuclear power plants, surgical operating rooms, etc. The ideal situation in any environment would be to create the least numbers of alarms necessary in order to avoid confusion. This paper examines issues as to the choice of presenting alarms under certain conditions. Exemplars include current alerting systems in aviation and other domains with implications for training

Roboleader: An Agent For Supervisory Control Of Multiple Robots

We developed an intelligent agent, RoboLeader, that could assist human operators in route planning for a team of ground robots. We compared the operators\u27 target detection performance in the 4-robot and 8-robot conditions. Results showed that the participants detected significantly less targets and had significantly worse situation awareness when there were 8 robots compared to the 4-robot condition. Those participants with higher spatial ability detected more targets than did those with lower spatial ability. Participants\u27 self-assessed workload was affected by the number of robots under control, their gender, and their attentional control ability. © 2010 IEEE

RoboLeader

We developed an intelligent agent, RoboLeader, that could assist human operators in route planning for a team of ground robots. We compared the operators\u27 target detection performance in the 4-robot and 8-robot conditions. Results showed that the participants detected significantly less targets and had significantly worse situation awareness when there were 8 robots compared to the 4-robot condition. Those participants with higher spatial ability detected more targets than did those with lower spatial ability. Participants\u27 self-assessed workload was affected by the number of robots under control, their gender, and their attentional control ability. © 2010 IEEE

Effects Of Unreliable Automation And Individual Differences On Supervisory Control Of Multiple Ground Robots

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\u27s 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\u27s 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\u27 attentional control impacted their overall multitasking performance, especially during their execution of the secondary tasks (communication and gauge monitoring). Copyright 2011 ACM

Designing For Mixed-Initiative Interactions Between Human And Autonomous Systems In Complex Environments

The purpose of the paper is to discuss human centered design implications for shared decision making between humans and autonomous systems in complex environments. Design implications are generated based on empirical results from two research paradigms. In the first paradigm, an intelligent agent (Robo Leader) supervised multiple subordinate systems and was in turn supervised by the human operator. The Robo Leader research varied number of subordinate units, task difficulty, agent reliability, type of agent errors, and partial autonomy. The second paradigm involved human interaction with partially and fully autonomous systems. Design implications from both paradigms are evaluated-relating to multitasking, adaptive systems, false alarms, individual differences, operator trust, and allocation of human and agent tasks for partial autonomy. We conclude that mixed-initiative decision sharing depends on designing interfaces that support human-Agent transparency