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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).

The ROV ground control simulator (Fig. 1) used in this multi-sensory research consists of two workstations: pilot and SO. At the left workstation, the pilot controls ROV flight (via stick-and-throttle inputs as well as invoking auto-holds), manages subsystems, and handles external communications. From the right workstation, the SO is responsible for locating and identifying points of interest on the ground by controlling cameras mounted on the ROV. Each station has an upper and a head-level 17″ color CRT display, as well as two 10″ head-down color displays. The upper CRT of both stations displays a ‘God's Eye’ area map (fixed, north up) with overlaid symbology identifying current ROV location, flight waypoints, and current sensor footprint. The head-level CRT (i.e., “camera display”) displays simulated video imagery from cameras mounted on the ROV. Head-up display (HUD) symbology is overlaid on the pilot's camera display and sensor specific data are overlaid on the SO's camera display. The head-down displays present subsystem and communication information as well as command menus. The simulation is hosted on four dual-Pentium PCs. The control sticks are from Measurement Systems Inc. and the throttle assemblies were manufactured in-house.

UAVs have been used by military forces since at least the War of Attrition – fought between Egypt and Israel between 1967 and 1970 – when the Israeli Army modified radio-controlled model aircraft to fly over the Suez Canal and take aerial photographs behind Egyptian lines (Bolia, 2004). Although the Israelis ill advisedly abandoned the concept before the Yom Kippur War, it was taken up by several nations in the ensuing decades, and today UAVs are regarded as a routine component of surveillance operations, having played a significant role in both Afghanistan and Iraq.

The most basic solution for monitoring position and attitude of an UA is through direct line-of-sight. Because they are usually standing outside, a pilot that maintains direct line-of-sight with the aircraft is usually referred to as the EP, as opposed to an internal pilot (IP) who obtains position and attitude information electronically while inside of a ground control station (GCS). Flight using an EP represents the most basic solution to the problem of separating the pilot from the aircraft while still enabling the pilot to monitor the location and attitude of the aircraft. Pilot perspective is changed from an egocentric to an exocentric point of view. Maintaining visual contact with the UA, the EP can control the aircraft using a hand-held radio control box. Many of these control boxes are similar to those used by radio-controlled aircraft hobbyists and provide direct control of the flight surfaces of the aircraft through the use of joysticks on the box. Very little automation is involved in the use of such boxes, which control the flight surfaces of the aircraft.

The successful culmination of missions based on Unmanned Aerial Vehicles (UAV) can be measured with two main parameters: (1) successful mission completion: all objectives of the mission (e.g., maneuvering and navigation, reconnaissance and targeting or search and rescue, and return) were accomplished and (2) safety: no damage to the vehicle and no fatalities or injuries to any human were sustained throughout the mission. Automation of the UAV's control and operations increasingly becomes a determining factor in successful mission completion and increased safety. However, in this day and age of automatically launched and retrieved swarms of UAVs, the human operator still has a critical role. Human-controlled UAVs will persist for a long time and human error is a factor that still needs addressing in the age of automation. Even a single person, who has flown radio-controlled model aircraft as a hobby since childhood, can still cause the crash of an expensive UAV in a matter of seconds. Moreover, there are aspects of human error in UAV control that can have important implications to the implementation of automation and to keeping the human operator in the control loop.

The most important advance in system design is the development of modeling and simulation methods to predict complex performance before prototypes are developed. New systems are developed in a spiraling approach; as more is learned about the system, design changes are proposed and evaluated. This approach allows the engineering team to “spin out” early versions of the system for preliminary evaluation, permitting changes to be made to the system design without incurring unacceptable cost. Because of the complexity of human performance, current modeling techniques provide only a first approximation. However, it has been demonstrated that even simple, inexpensive modeling approaches are useful in uncovering workload and performance problems related to developing systems (Barnes & Beevis, 2003). More important, these models can serve as the basis for operator simulation experiments that verify and also calibrate the original models. Furthermore, early field tests and system of systems demonstrations that can validate these results under actual conditions are becoming an increasingly significant part of the early design process. Fig. 1 illustrates this interdependence indicating a spiraling process throughout the design starting with simple predictive methods and progressing to more expensive validation methods. These iterations should continue until most of the soldier's variance is accounted for, and before any formal soldier testing is conducted. Fig. 1 presents the ideal combination of techniques; not all systems can be evaluated this thoroughly but more cost-effective modeling and simulation tools combined with realistic field exercises should make this approach more the norm as future unmanned systems are developed (Barnes & Beevis, 2003). In the remainder of this chapter, several case studies are presented to illustrate how the techniques in Fig. 1 have been applied in UAV programs.

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).

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).

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.

Imagine trying to navigate through your environment while looking straight ahead through a narrow tube. These are essentially the conditions the operator of an ROV in teleoperation mode may have to contend with. We hypothesized that controlling ground-based ROVs would be easier if an operator developed an explicit overview of the space in which the ROV was maneuvering. Accordingly, we conducted a study in which we had naive undergraduate participants explore a maze-like virtual desert environment while drawing a map of the area. After completing a 30-min mapping task, participants re-entered the maze to search for and retrieve a target object. The virtual robots and landscape, which had minimal landmarks and a maze of navigable paths (see Fig. 1), were created using CeeBot (see Chadwick, Gillan, Simon, & Pazuchanics, 2004, for a discussion of the CeeBot tool). Maps drawn by participants were rated independently by three raters (graduate psychology students) for the usefulness of the map for navigating the area on a scale from 1 (not at all useful) to 7 (extremely useful). Cronbach's alpha was computed as a consistency estimate of inter-rater reliability at 0.96.