Automated Scheduling Decision Support for Supervisory Control of Multiple UAVs

Inthefuturevisionofallowingasingleoperatortocontrolmultipleunmannedvehicles(on land, in the air, or under water), it is not well understood how multiple vehicle control will affect operator workload, and what automated decision support strategies will improve, or possible degrade, operator performance. To this end, this paper presents the results of an experiment in which operators simultaneously managed four highly autonomous independent homogenous UAVs in a simulation, with the overall goal of destroying a predetermined set of targets within a limited time period.The primary factors under investigation were increasing levels of automation from manual to management-by-exception, manifested through a timeline visualization. Increasing levels of automation can reduce workload but they can also result in situation awareness degradation as well as complacency. This human-in-the-loop experiment revealed that when provided with a high workload preview visualization as well as automated recommendations for workload mitigation, operators becamefixatedontheneedtogloballyoptimizetheirschedules,anddidnotadequatelyweigh uncertainty in their decisions.These behaviors significantly degraded operator performance to the point that operators without any decision support performed better than those with probabilistic prediction information and the ability to negotiate potential outcomes.

[1]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[2]  Liling Ren,et al.  Interaction of Automation and Time Pressure in a Route Replanning Task , 2002 .

[3]  Thomas B. Sheridan,et al.  Human and Computer Control of Undersea Teleoperators , 1978 .

[4]  A. Tversky,et al.  Judgment under Uncertainty , 1982 .

[5]  David B. Kaber,et al.  Design of Automation for Telerobots and the Effect on Performance, Operator Situation Awareness, and Subjective Workload , 2000 .

[6]  Philip J. Smith,et al.  Design of a Cooperative Problem-Solving System for En-Route Flight Planning: An Empirical Evaluation , 1994 .

[7]  P M Todd,et al.  Précis of Simple heuristics that make us smart , 2000, Behavioral and Brain Sciences.

[8]  Mica R. Endsley,et al.  Analysis of Situation Awareness from Critical Incident Reports , 2000 .

[9]  Penelope M. Sanderson,et al.  The Human Planning and Scheduling Role in Advanced Manufacturing Systems: An Emerging Human Factors Domain , 1989 .

[10]  Colin G. Drury,et al.  Human optimization with moving optima , 1989 .

[11]  Cathleen Wharton,et al.  The cognitive walkthrough method: a practitioner's guide , 1994 .

[12]  Mary L. Cummings,et al.  The Need for Command and Control Instant Message Adaptive Interfaces: Lessons Learned from Tactical Tomahawk Human-in-the-Loop Simulations , 2004, Cyberpsychology Behav. Soc. Netw..

[13]  Christopher D. Wickens,et al.  TASKILLAN II: Pilot Strategies for Workload Management , 1990 .

[14]  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.

[15]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[16]  Thomas B. Sheridan,et al.  Dynamic Decisions and Work Load in Multitask Supervisory Control , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Amy R. Pritchett,et al.  Development and Evaluation of a Cockpit Decision-Aid for Emergency Trajectory Generation , 2001 .

[18]  Ravi S. Adapathya,et al.  Strategic Behavior, Workload, and Performance in Task Scheduling , 1991 .

[19]  R Parasuraman,et al.  Designing automation for human use: empirical studies and quantitative models , 2000, Ergonomics.

[20]  Mica R. Endsley,et al.  Design and Evaluation for Situation Awareness Enhancement , 1988 .