Automation and Robotics
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Current Research
Advanced automation and robots are being increasingly introduced into many complex human-machine systems in such domains as aviation, military command and control, health care, and civilian search and rescue applications. Our current research on human-automation interaction is focused on understanding the effects of different levels and types of automation (Parasuraman, Sheridan, & Wickens, 2000) on human operator attention, decision-making, and other aspects of cognition. We are also examining how adaptive automation can be designed effectively so as to be sensitive to changes in operational context and human operator workload. Of particular interest is the development of delegation interfaces (Miller & Parasuraman, 2007) as a form of adaptable automation.
We are examining these issues using a number of different simulators and microworlds. These range from a simple flight simulation task battery to a high-fidelity simulation of semi-autonomous robotic (aerial and ground) vehicles. See Software.
The following are our currently active research projects in automation and robotics:
Adaptive Delegation Interfaces for Human-Robot Teaming (Army Research Laboratory, subcontract, Perceptronics Solutions)
Quantitative models of human dynamic attention allocation (Air Force Research Laboratory, subcontractor to Aptima, Inc.)
Supporting battle management command and control: Designing innovative interfaces and selecting skilled operators (Air Force Research Laboratory)
Adaptive automation architecture documentation (Army Research Laboratory)
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Hardware
ASL Series 5000 Eye Tracking System
ASL Model is a complete eye tracking system for use in situations where the subject can wear lightweight, head mounted optics and must have unrestricted freedom of movement. The optics are lightweight and mounted on an adjustable headband. The scene is recorded with a color camera that can be mounted on the headband or on a fixed tripod. The images from the eye and scene cameras are displayed on two external 9” monitors. ASL EYEPOS operating software and EYENAL off-line data analysis software programs are used for eye-tracking analysis on a PC computer.
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Software
Multi-Attribute Task (MAT) Battery
http://www.openchannelfoundation.org/projects/MAT/
MAT, a Multi-Attribute Task battery, gives the researcher the capability of investigating issues of automation reliability and task load on multi-task performance. The battery provides a benchmark set of tasks for use in a wide range of laboratory studies of operator performance and workload. MAT incorporates tasks analogous to activities that aircraft crew members perform in flight, while providing a high degree of experiment control, performance data on each subtask, and freedom to use non-pilot test subjects. Research with the MAT task at the Arch Lab has examined the effects of unreliable on attention and performance.
Sensor to Shooter (STS)
The Sensor to Shooter Task Environment is a PC-based low-fidelity software simulation of a battlefield-based sensor to shooter targeting system (STS). The Sensor to Shooter application is used to examine questions regarding the levels and stages of automation and the impact of reliability on human performance. Research with the STS at the Arch Lab has examined the effects of reliability and automation stages and levels on human performance (e.g., Rovira, McGarry, & Parasuraman, 2007).
Simulation Integration Laboratory (SIL)
The SIL is a high-fidelity simulation of a one-person crew station that allows the operator to control robotic assets semi-autonomously or by teleoperation. The assets are Uninhabited Ground Vehicles (UGVs) and Uninhabited Aerial Vehicles (UAVs). The UGV can be operated autonomously or piloted remotely and carries visual sensors to capture images of terrain in the area of operation. The main purpose of these robotic assets is to gather Reconnaissance, Surveillance, and Target Acquisition (RSTA) information for use by mounted combat teams. Research with the SIL at the Arch Lab explored questions on automation reliability and joint human-automation performance (e.g., de Visser, Parasuraman, & Cosenzo, 2007).
SynWin
SynWin is a PC-based multiple-task battery that represents a sample of the perceptual and cognitive skills required in many forms of complex work. SynWin includes four tasks that can be presented simultaneously to the participant in any combination: a simple memory task, an arithmetic computation task, a visual monitoring task, and an auditory monitoring task.
Robotic NCO
The Robotic NCO simulation is a PC-based one-person crew supervisor station for uninhabited air and ground vehicles operations and related communications. The Robotic NCO was designed by George Mason University to isolate some of the cognitive requirements associated with a single operator controlling robotic assets within a larger military environment. Research with the Robotic NCO at the Arch Lab as examined research questions on adaptive automation, change detection, situation awareness, and mental workload.
Roboflag
http://roboflag.mae.cornell.edu/
The RoboFlag simulation environment is a PC-based application developed by Cornell University. Roboflag was designed to accurately capture the actions and states of hardware robots. The RoboFlag simulation was modified to allow a single participant (blue team) to compete against an automated opponent (red team) operating under scripted procedures. Research conducted with the Roboflag environment at the Arch Lab has examined research questions on automation architectures, levels of automation, and task switching (e.g., Parasuraman, Galster, Squire, Furukawa, & Miller, 2005).
Dynamic Distributed Decision- DDD
http://dddweb.aptima.com/modules/news/
The Dynamic Distributed Decision-making (DDD) environment is an Aptima’s simulation test bed. Used in more than 30 military, academic, and commercial laboratories for teamwork and taskwork studies, the DDD allows researchers to select and rapidly reconfigure scenarios that create challenging situations for command and control (C2) operators and leaders. The DDD was designed to capture the essential elements of many different team tasks, and to allow the experimenter to vary team structure, access to information, and control of resources.
Park Asset Monitoring and Management Interface (PAMMI)
The Park Asset Monitoring and Management Interface (PAMMI) is a Smart Information Flow (SIFT) simulation test bed. The PAMMI application is used to examine question on decisions aids and automation etiquette.
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Recent Publications
de Visser, E., Parasuraman, R., & Cosenzo, K. (2007, March). Effects of imperfect automation on human supervision of multiple uninhabited vehicles. Paper presented at the Annual Meeting of Division 21 of the American Psychological Association, George Mason University, Fairfax, VA.
Miller, C., & Parasuraman, R. (2007). Designing for flexible interaction between humans and automation: Delegation interfaces for supervisory control. Human Factors, 49, 57-75.
Rovira, E., McGarry, K., & Parasuraman, R. (2007). Effects of imperfect automation on decision making in a simulated command and control task. Human Factors, 49,76-87.
Metzger, U., & Parasuraman, R. (2006). Effects of automated conflict cueing and traffic density on air traffic controller performance and visual attention in a datalink environment. International Journal of Aviation Psychology, 16, 343-362.
Sheridan, T., & Parasuraman, R. (2006). Human-automation interaction. Reviews of Human Factors and Ergonomics, 1, 89-129.
Metzger, U., & Parasuraman, R. (2005). Automation in future air traffic management: Effects of decision aid reliability on controller performance and mental workload. Human Factors, 47(1), 35-49.
Parasuraman, R., Galster, S., Squire, P., Furukawa, H., & Miller, C. (2005). A flexible delegation interface enhances system performance in human supervision of multiple autonomous robots: Empirical studies with RoboFlag. IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems and Humans, 35(4), 481-493.
Parasuraman, R., & Miller, C. (2004). Trust and etiquette in high-criticality automated systems. Communications of the Association for Computing Machinery,47(4), 51-55.
Masalonis, A. J., & Parasuraman, R. (2003). Fuzzy signal detection theory: Analysis of human and machine performance in air traffic control, and analytic considerations. Ergonomics, 46, 1045-1074.
Lorenz, B., Di Nocera, F., Roettger, S., and Parasuraman, R. (2002). Automated fault management in a simulated space flight micro-world. Aviation, Space, & Environmental Medicine, 73, 886-89
Galster, S., Duley, J. A., Masalonis, A., & Parasuraman, R. (2001). Air traffic controller performance and workload undermature Free Flight: Conflict detection and resolution ofaircraft self-separation. International Journal of Aviation Psychology, 11, 71-93.
Metzger, U., & Parasuraman, R. (2001). The role of the air traffic controller in future air traffic management: An empirical study of active control versus passive monitoring. Human Factors, 43, 519-528.
Parasuraman, R. (2000). Designing automation for human use: Empirical studies and quantitative models. Ergonomics, 43, 931-951.
Parasuraman, R., Masalonis, A. J., & Hancock, P. A. (2000). Fuzzy signal detection theory: Basic postulates and formulas for analyzing human and machine performance. Human Factors, 42, 636-659.
Parasuraman, Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems and Humans, 30, 286-297.
Sheridan, T. B., & Parasuraman, R. (2000). Human vs. automation in responding to failures: An expected-value analysis. Human Factors, 42, 403-407.
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