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The great success of unmanned aerial vehicles (UAVs) in performing near-real time tactical, reconnaissance, intelligence, surveillance and other various missions has attracted broad attention from military and civilian communities. A critical contribution to the increase and extension of UAV applications, resides in the separation of pilot and vehicle allowing the operator to avoid dangerous and harmful situations. However, this apparent benefit has the potential to lead to problems when the role of humans in remotely operating “unmanned” vehicles is not considered. Although, UAVs do not carry humans onboard, they do require human control and maintenance. To control UAVs, skilled and coordinated work of operators on the ground is required.

SA has been described as “generating purposeful behavior.” that is, behavior that is directed toward a task goal (Smith & Hancock, 1995). It involves being aware of what is happening around you and understanding what occurring events mean with respect to your current and future goals. Endsley (1995) has formally defined SA as the “perception of elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future” (Endsley, 1995, p. 36). SA has been hypothesized as being critical to operator task performance in complex and dynamic operations (Salas, Prince, Barker, & Shrestha, 1995), like tasking and controlling remotely operated systems. Operators in remote control of ground vehicles need to be aware of where the vehicle is, what the vehicle is doing, and how activities as part of the overall task lead to accomplishment of mission goals. They must also consider the health of the overall system and how the environment affects vehicle status and the ability to complete tasks. In studying robot control in simulated USAR operations, Drury, Scholtz, and Yanco (2003) observed that most of the problems encountered when navigating robots resulted from the human's lack of awareness of these elements.

A fundamental issue driving much of the current research is the design of the interface between humans and ROVs. Autonomous robots are sufficiently different from most computer systems as to require new research and design principles (Adams & Skubic, 2005; Kiesler & Hinds, 2004). Previous work on coordination between humans and automated agents has revealed both benefits and costs of automation for system performance (Parasuraman & Riley, 1997). Automation is clearly essential for the operation of many complex human–machine systems. But in some circumstances automation can also lead to novel problems for operators. Automation can increase workload and training requirements, impair situation awareness and, when particular events co-occur in combination with poorly designed interfaces, lead to accidents (e.g., Degani, 2004; Parasuraman & Riley, 1997).

As they are currently conducted, missions by single ROVs consist of several sub-tasks. After a vehicle has been launched, a human operator or a small team is responsible for controlling the flight, navigation, status monitoring, flight and mission alteration, problem diagnosis, communication and coordination with other operators, and often data analysis and interpretation. These tasks are similar in terms of their locus of control (e.g., keyboard and mouse input, joystick, trackball, visual display).

Cognitive Load Theory (CLT) is the product of over a decade of research in the instructional science domain (Chandler & Sweller, 1991; Sweller & Chandler, 1994), and its applications to other areas of inquiry continues to expand (see Cuevas, Fiore, & Oser, 2002; Paas, Renkl, & Sweller, 2003a; Paas, Tuovinen, Tabbers, & Van Gerven, 2003b; Scielzo, Fiore, Cuevas, & Salas, 2004). The core of CLT is based on two sets of what are termed cognitive load factors that are either endogenous or exogenous from the viewpoint of an operator interacting with the environment. Endogenous (or intrinsic) factors are sources of cognitive load in terms of the general amount and complexity of information with which the operator has to interact. In training environments, intrinsic load is directly proportional to the amount of materials that trainees need to acquire. As such, the more complex the information is in terms of volume and conceptual interactivity, the higher the cognitive load will be. In operational settings, high intrinsic load can occur whenever informational demands that need to be processed are high. Within the context of human–robot team environments, there is likely to be unique intrinsic load factors emerging from this hybrid teamwork interaction (e.g., information produced by synthetic team members). Another source of cognitive load comes from exogenous or extraneous factors. In training and operational settings alike, extraneous cognitive load may occur dependent upon the manner in which information needing attention is presented. Specifically, the more complex the human–robot team interface is in relation to the process by which information is displayed and/or communicated, the more extraneous cognitive load can be present. For example, the technological tools involved in the communication of information, and the associated modalities used to process information may inadvertently result in cognitive load. Simply put, high extraneous cognitive load can be produced as a result of using sub-optimal information presentation and communication. Overall, exogenous factors can stem from the added complexity of human–robot operations in terms of distinct command-and-control systems that emerge from using novel technology. Within such operations, it is particularly important to control sources of extraneous cognitive load that have been shown to produce two distinct negative effects on information processing – redundancy of information and split-attention. These have been shown to attenuate processing capacity thereby minimizing optimal information processing (e.g., Sweller, 1994; Mayer, 1999).

Computer simulation is a test-bed for research that began in the 1970s, grew tremendously in popularity in the 1990s, and has since continued to mature in complexity and realism. When computer simulations were in their infancy, their biggest advantage was the ability to have complete control over the environment in which the simulation took place. Missions could be changed from daylight to twilight with a few keystrokes. Weather conditions could be altered or inserted based on the needs of the experiment. Perhaps most importantly, the landmasses in which the simulations took place were boundless, in their cyber world.

UAVs or unmanned (or the more politically correct, “unpiloted” or “uninhabited”) Aerial Vehicles and the broader class of remotely operated vehicles (ROVs) have attracted much attention lately from the military, as well as the general public. Generally, ROVs are vehicles that do not carry human pilots or operators, but instead are controlled remotely with different degrees of autonomy on the part of the vehicle. The role of UAVs in the military has rapidly expanded over the years such that every branch of the U.S. military deploys some form of UAV in their intelligence, surveillance, and reconnaissance operations. Recent U.S. military successes include a USAF Predator UAV operating in Iraq, but piloted by a team at Nellis AFB (now Creech AFB) in Las Vegas, Nevada, which successfully aided in finding Saddam Hussein (Rogers, 2004). Another more recent example took place in August 2004 when a Predator UAV armed with Hellfire missiles, also controlled from Nellis AFB, rescued a group of U.S. Marines pinned down by sniper fire in Najaf, Iraq (CNN, 2005). The value of UAVs is recognized by other nations as well who have active UAV programs including, but not limited to, Germany, England, China, France, Canada, South Africa, and Israel.