Measuring Shared Mental Models in Unmanned Aircraft Systems

Measuring Shared Mental Models in Unmanned Aircraft Systems

Rosemarie Reynolds (Embry Riddle Aeronautical University, USA), Alex J. Mirot (Embry Riddle Aeronautical University, USA) and Prince D. Nudze (Embry Riddle Aeronautical University, USA)
DOI: 10.4018/978-1-4666-5888-2.ch113
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Abstract

Unmanned aircraft systems (UASs) are becoming part of the aviation landscape, taking on the dirty, dangerous, or dull operations traditionally completed by military and specialized civil aircraft. These operations often require high levels of team coordination. Team coordination is facilitated when team members share a mental model of group tasks and the individual crewmember's responsibilities in the performance of these tasks. The shared mental model is therefore critical for unmanned aircraft system teams to complete their operational objectives. The ability to forge a shared mental model is complicated by the diverse and often distributed nature of unmanned aircraft system teams. Before strategies can be developed to create accurate shared mental models, researchers must effectively measure shared mental models. This chapter explores the measurement of shared mental models in UASs.
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Background

History of Unmanned Aviation Systems

As early as 425 B.C., humanity was dreaming of unmanned flight. Inventors and engineers from Archytas of Tarantine to Leonardo Da Vinci attempted to create unmanned aircraft. Yet true unmanned aircraft systems (UASs) did not originate until the mid-20th century (Dalamagkidis, Valavanis & Piegl, 2012; Keane & Carr, 2013). The reasons for the delay were three technological hurdles that had to be overcome: 1) remote control; 2) flight stabilization, and 3) autonomous navigation (Newcome, 2004).

The first hurdle was tackled in 1898, when Nikola Tesla demonstrated what he coined “telautomation” at the Electrical Exposition, in which he was able to remotely pilot a boat around a tank of water. Elmer Sperry, who invented the aircraft gyrostabilizer, designed to level the wings of the aircraft in the absence of pilot input, addressed the second hurdle, which was stabilized flight, in 1909 (Newcome, 2004).

Looking for a way to reduce heavy losses sustained by the air forces in World War I, the United Kingdom and the United States began experimenting with unmanned systems capable of flying to a target and exploding. Aviation pioneers such as Kettering, Hewitt, Sperry, and Low found ways to convert traditional aircraft using gyrostabilizer and remote actuation. These early systems, such as Aerial Target and Kettering Bug, were crude and unable to navigate a pre-programmed flight plan autonomously and therefore not considered fully capable UASs (Keane & Carr, 2013; Newcome, 2004).

Between World War I and World War II, the British Navy, the U.S. Navy, and U.S. Army continued to foster the testing and development of unmanned systems. From 1934-1943, the Fairey aircraft company produced over 400 Fairey Queen Bees, a converted De Havilland Tiger Moth controlled by a simple remote control station using a simple rotary dial to command changes in direction, altitude, and speed (Braithwaite, 2012). World War II brought about the German built V-1 rocket. The V-1 used a pulse jet propulsion system and a very crude guidance system that used barometric pressure to maintain altitude, a directional gyro to maintain heading, and an anemometer to calculate distance travelled (Newcome, 2004). Like the Kettering Bug, the V-1 crews would calculate the distance to the target and then pre-program the aircraft (Austin, 2010).

It was not until a few years after World War II when Charles Draper solved the autonomous navigation problem with the invention of inertial navigation systems (Newcome, 2004). With all the technology in place, the stage was set for modern UASs. The Defense Advanced Projects Agency (DARPA) continued to invest in UAS research with the most significant program being the Amber UAS. Amber was a joint project for the U.S. Navy and DARPA, aimed at creating a medium altitude long endurance UAS (Ehrhard, 2010). In 1995, Amber’s latest iteration the Predator was deployed, and is still in service.

Key Terms in this Chapter

Distributed Team: A distributed team is a team whose members are not collocated.

Shared Mental Model: A mental model that is shared among team members, and may include: 1) task-specific knowledge, 2) task-related knowledge, 3) knowledge of teammates and 4) attitudes/beliefs.

Cognitive Load: The amount of mental demand associated with a task.

Unmanned Aircraft Systems (UAS): An unmanned vehicle and operator, which, in addition, may include launch and recovery ersonale, mission control personaell, payload operator, and ground support units.

Communication Analysis: Communication analysis uses a variety of techniques, which not only focus on words spoken, but may also consider other features such as intonation, pitch, tempo, and nonverbal cues such as posture and gesturing.

Team Coordination: In a team task, the activities involved in making sure that the right things happen at the right time is known as coordination. This may involve communication, but may also include standardized procedures and mission preplanning.

Mutual Organizational Awareness: In communication analysis, mutual organizational awareness refers to the extent to which team members are aware of the activities of other team members.

Mental Model: A network of knowledge content, as well as the relationships among the content.

Intensive Interdependence: A form of team interdependence characterized by simultaneous collaboration and problem solving.

Anticipation Ratio: In communication analysis, the anticipation ratio refers to the number of communications involving the transfer of information to the number of communications in which information is requested.

Elicitation Method: The method used to determine mental model content; these include: 1) cognitive interviewing; 2) verbal protocol analysis; 3) content analysis; 4) observation of task performance, 5) visual card sorting, 6) repertory grid technique (RGT); 7) causal mapping; 8) pairwise rating methods; and 9) ordered tree technique.

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