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188 Int. J. Applied Decision Sciences, Vol. 3, No. 3, 2010 Copyright © 2010 Inderscience Enterprises Ltd. Global vs. local decision support for multiple independent UAV schedule management Mary L. Cummings* Aeronautics and Astronautics Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Fax: 617-253-4196 E-mail: [email protected] *Corresponding author Amy S. Brzezinski Expedition Vehicle Division, NASA Johnson Space Center, Houston, TX 77058, USA E-mail: [email protected] Abstract: As unmanned aerial vehicles (UAVs) become increasingly autonomous, time-critical and complex single-operator systems will require advance prediction and mitigation of schedule conflicts. However, actions that mitigate a current schedule conflict may create future schedule problems. Decision support is needed allowing an operator to evaluate different mission schedule management options in real-time. This paper describes two decision support visualisations for single-operator supervisory control of four independent UAVs performing a time-critical targeting mission. A configural display common to both visualisations, called StarVis, graphically depicts current schedule problems, as well as projections of potential local and global schedule problems. Results from an experiment showed that subjects using the locally optimal StarVis implementation had better performance, higher situational awareness, and no significant increase in workload over a more globally optimal implementation of StarVis. This research effort highlights how the same decision support design applied at different abstraction levels can produce different performance results. Keywords: multiple unmanned aerial vehicles; supervisory control; configural displays; decision support; schedule; visualisation. Reference to this paper should be made as follows: Cummings, M.L. and Brzezinski, A.S. (2010) ‘Global vs. local decision support for multiple independent UAV schedule management’, Int. J. Applied Decision Sciences, Vol. 3, No. 3, pp.188–205. Biographical notes: Mary L. Cummings received her BS in Mathematics from the US Naval Academy in 1988, her MS in Space Systems Engineering from the Naval Postgraduate School in 1994, and her PhD in Systems Engineering from the University of Virginia in 2004. A Naval Officer from 1988–1999, she was one of the Navy’s first female fighter pilots. She is currently an Associate Professor in the Aeronautics & Astronautics Department at the Massachusetts Institute of Technology. Her research interests include human interaction withGlobal vs. local decision support for multiple independent UAV 189 autonomous vehicle systems, modelling human interaction with complex systems, decision support design, and the ethical and social impact of technology. Amy Brzezinski received her SB in 2005 and SM in 2008 in Aeronautical & Astronautical Engineering from the Massachusetts Institute of Technology. She is currently a NASA Flight Controller for the International Space Station in the Command and Data Handling Group. Her research interests include display design, and space and ground-control human factors. 1 Introduction As unmanned aerial vehicle (UAV) flight and navigation tasks become more automated, UAV missions will transition from teams of people operating one UAV to one person supervising multiple UAVs (Cummings et al., 2007). Increases in UAV autonomy will alter the human operator’s role to one of supervisory control, in which the operator will be primarily responsible for high-level mission management as opposed to low-level tasking and manual flight control. Because of this reduction in tasks requiring direct human control, a UAV operator may be able to supervise and divide attention across multiple UAVs. In this scenario, a critical human factors issue is one of mental workload, which is a function of attention allocation across numerous tasks and the ability to quickly and accurately switch between tasks (Crandall and Cummings, 2007). Additionally, the effect of increased automation and workload on an operator’s situational awareness is an area of concern. While increased automation will be necessary to achieve the one operator-multiple UAV control paradigm, automation can increase an operator’s mental workload and decrease situation awareness (SA) due to opacity, lack of feedback, and mode confusion (Billings, 1997; Parasuraman et al., 2000). An experiment involving a multiple UAV simulation was conducted to explore how decision support tools could help an operator cope with high workload periods while supervising multiple UAVs. Specifically, the ability of configural decision support visualisations (DSVs) to help operators proactively manage their schedule and increase the overall system’s performance was studied. 2 Background In a single operator-multiple UAV mission, it is likely that more than one UAV will require the operator’s attention in simultaneous mission-critical tasks, thus creating potential high workload periods. In order to investigate this and other related issues, a simulation test bed was created, called the multi-aerial unmanned vehicle experiment (MAUVE) test bed. Using MAUVE, one human operator supervises four UAVs in a time-critical targeting mission. MAUVE consists of a map and timeline-decision support display (Figure 1). In addition to the geo-spatial representation, the map display includes a UAV interaction panel allowing operators to send commands to the UAVs. The timeline-decision support display includes a colour-coded timeline representing all four UAV schedules for the next 15 minutes, as well as an instant messaging window for190 M.L. Cummings and A.S. Brzezinski human-human communications and a UAV datalink window for human-UAV communication. Depending on the level of automation used in the simulation, mission management recommendations are provided to the right of the timelines. Figure 1 The MAUVE interface with the (left) map display and the (right) decision support timeline (see online version for colours) MAUVE presents pre-planned missions to the operator for real-time execution. Operator mission tasks during the simulation consist of arming and firing UAV payloads at scheduled times, replanning UAV paths in response to emergent threats, assigning emergent targets to the most appropriate UAV, and answering questions about the mission


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