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Fig. 1 presents the interface used by our pilots to fly the UAV simulation.

This chapter provides an overview of the Department of Defense (DoD) laboratory structure to help equipment designers, modelers, and manufacturers determine where research, testing programs, or relevant findings can be found. The chapter includes a discussion of the performance measures and metrics typically used in DoD laboratories and concludes by considering the current state-of-the-art as well as the state-of-the-possible for human performance measurement.

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).

To understand the importance of coordination and collaboration for ROV teams, let us examine some of the typical tasks that ROV operators might be required to perform (Cooke & Shope, 2004; Gugerty, DeBoom, Walker, & Burns, 1999). To do so, we will use the members of a U.S. Air Force Predator crew as an example. The team consists of three members: an Air Vehicle Operator (AVO) who pilots the aircraft, a Payload Operator (PLO) who operates the surveillance equipment, and a Data Exploitation, Mission Planning, and Communications Operator (DEMPC) who is responsible for mission planning. In the course of a mission, the AVO is responsible for the take off and landing of the aircraft. Because they fly the aircraft from a remote location, AVOs are generally required to use visual input from a camera mounted on the nose of the aircraft to guide their flight. Once in the air, the PLO can operate cameras and sensors mounted on the belly of the plane to gather information. The DEMPC, who is in contact with the upper echelons of the organization, provides the AVO with the desired heading and the PLO with target coordinates.

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).

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.

DoD accidents are classified according to the severity of injury, occupational illness, and vehicle and/or property damage costs (Department of Defense, 2000). All branches of the military have similar accident classification schemes, with Class A being the most severe. Table 1 shows the accident classes for the Army. The Air Force and Navy definitions of Class A–C accidents are very similar to the Army's definition. However, they do not have a Class D. As the total costs of some Army UAVs are below the Class A criteria ($325,000 per Shadow aircraft; Schaefer, 2003), reviewers have begun to add Class D data into their analyses (Manning, Rash, LeDuc, Noback, & McKeon, 2004; Williams, 2004).

If these tasks are broken down by aircrew position, the pilot (often called the operator) is the prime command and control coordinator, while the second crew member (the sensor operator) is responsible for collecting, processing and communicating sensor data. There is often an overlap of duties between these two crew members, but the operation of smaller UAVs is commonly only controlled by these two personnel. An illustration of the ground control shelter interface used by a typical Army UAV pilot (AVO, Air Vehicle Operator) for the US Army Shadow UAV follows (Fig. 1).

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.