Mission Control of Multiple Unmanned Aerial Vehicles: A Workload Analysis

With unmanned aerial vehicles (UAVs), 36 licensed pilots flew both single-UAV and dual-UAV simulated military missions. Pilots were required to navigate each UAV through a series of mission legs in one of the following three conditions: a baseline condition, an auditory autoalert condition, and an autopilot condition. Pilots were responsible for (a) mission completion, (b) target search, and (c) systems monitoring. Results revealed that both the autoalert and the autopilot automation improved overall performance by reducing task interference and alleviating workload. The autoalert system benefited performance both in the automated task and mission completion task, whereas the autopilot system benefited performance in the automated task, the mission completion task, and the target search task. Practical implications for the study include the suggestion that reliable automation can help alleviate task interference and reduce workload, thereby allowing pilots to better handle concurrent tasks during single- and multiple-UAV flight control.

[1]  N Moray,et al.  Fault management in process control: eye movements and action. , 1989, Ergonomics.

[2]  Gavriel Salvendy,et al.  Handbook of Human Factors and Ergonomics , 2005 .

[3]  Peter A. Hancock,et al.  Human-Centered Design of Unmanned Aerial Vehicles , 2003 .

[4]  Christopher D. Wickens,et al.  Imperfect Automation in Unmanned Aerial Vehicle Flight Control , 2003 .

[5]  The influence of aircraft proximity data on the subjective mental workload of controllers in the air traffic control task. , 1999, Ergonomics.

[6]  Arye R. Ephrath,et al.  Detection by Pilots of System Failures during Instrument Landings , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Joachim Meyer,et al.  Conceptual Issues in the Study of Dynamic Hazard Warnings , 2004, Hum. Factors.

[8]  David Shinar,et al.  New Alternative Methods of Analyzing Human Behavior in Cued Target Acquisition , 2003, Hum. Factors.

[9]  C D Wickens,et al.  Role of multiple resources in predicting time-sharing efficiency: evaluation of three workload models in a multiple-task setting. , 1995, The International journal of aviation psychology.

[10]  Mary L. Cummings,et al.  Developing Operator Capacity Estimates for Supervisory Control of Autonomous Vehicles , 2007, Hum. Factors.

[11]  Barrett S. Caldwell,et al.  Human Performance and Control of Multiple Systems , 1996, Hum. Factors.

[12]  Christopher D. Wickens,et al.  Comparing Quantitative Model Predictions to Experimental Data in Multiple-UAV Flight Control , 2003 .

[13]  David E. Kieras,et al.  A computational theory of executive cognitive processes and multiple-task performance: Part 2. Accounts of psychological refractory-period phenomena. , 1997 .

[14]  Christopher D. Wickens,et al.  Strategic workload management and decision biases in aviation , 1994 .

[15]  N. Moray,et al.  A simulation study of human performance deterioration and mental workload , 1993 .

[16]  Hans J.A.  Augmenting Camera Images for Operators of Unmanned Aerial Vehicles , 2003 .

[17]  Heath A. Ruff,et al.  Human Interaction with Levels of Automation and Decision-Aid Fidelity in the Supervisory Control of Multiple Simulated Unmanned Air Vehicles , 2002, Presence: Teleoperators & Virtual Environments.

[18]  Christopher D. Wickens,et al.  Attentional Models of Multitask Pilot Performance Using Advanced Display Technology , 2003, Hum. Factors.

[19]  Michael J. Barnes,et al.  Using Simulation Models to Analyze the Effects of Crew Size and Crew Fatigue on the Control of Tactical Unmanned Aerial Vehicles (TUAVs) , 2002 .

[20]  Christopher D. Wickens,et al.  Multiple resources and performance prediction , 2002 .