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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.

SD is defined as a failure to sense correctly the attitude, motion, and/or position of the aircraft with respect to the surface of the earth (Benson, 2003). The types of SD are generally thought to be “unrecognized” and “recognized” (Previc & Ercoline, 2004). Although a third type has been reported (incapacitating), this type seems irrelevant to UAV operations. Unrecognized SD occurs when the person at the controls is unaware that a change in the motion/attitude of the aircraft has taken place. The cause is often the result of a combination of sub-threshold motion and inattention. This type of SD is known to be the single most serious human factors reason for aircraft accidents today, accounting for roughly 90% of all known SD-related mishaps (Davenport, 2000). Recognized SD occurs when a noticeable conflict is created between the actual motion/attitude of the aircraft and any one of the common physiological sensory mechanisms (e.g., visual, vestibular, auditory, and tactile). Recognized SD is the most common type of SD, accounting for the remaining SD-related accidents.

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.

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.

Currently, the European air transport system is experiencing an annual growth of 7%. With an increasing number of flights, airports are reaching their capacity limits and are becoming a bottleneck in the system. Mantea is a European Commission funded project dealing with this issue. This paper focuses on planning decision support tools for airport traffic controllers.

The objective of our planning tools is to achieve a better use of the available airport infrastructure (taxiways and runways). To generate a safe plan, many rules must be taken into account that restrict the usage of airport tarmac: international regulations, airport operational procedures, aircraft performance, weather conditions and sometimes even controller “usual practices”. To generate a realistic plan, extensive monitoring of the traffic situation as well as suitable timing must be achieved. In the life cycle of a flight, 11 out of 15 possible causes of delay occur in an interval of 10-20 minutes, between aircraft start-up request and push-back. This means that precise planning before the end of this period is highly improbable. On the other hand, planning after this period implies the need for fast responses from the system.

In the Mantea project, an architecture is proposed in which a co-operative approach is taken towards planning aircraft movements at the airport. Controllers will be supported by planning tools that help assigning routes and departure times to controlled vehicles, in planning runway allocation (departure sequence) and occupancies, and in monitoring plan progress during flight phases. The planning horizon relates to medium term operations, i.e. 2-20 minutes ahead. The Mantea planning tools implement the following functions: runway departure planning, routing, and plan conformance monitoring. The tools will reduce the controller's workload, increase the level of safety for airport surface movements, and reduce the number of delays and operating costs for the airliners.

In this paper, we will focus on the constraint satisfaction programming techniques used in Mantea for (1) runway departure planning, (2) itinerary search and taxi planning functions. The airport tarmac and runway vicinity air routes have been modelled as a graph. Real time constraints have brought us to develop an algorithm linear in complexity for the itinerary search problem. Operational pressure has led us to develop fast search strategies for scheduling (i.e. use of heuristics, hill climbing…).