Neurophysiological and behavioral studies of human-swarm interaction tasks

This paper studies human-swarm interaction performance by evaluating both the neurophysiological and behavioral characteristics of human subjects. By utilizing our unique test facility, we conduct a series of real-world-scenario-inspired tasks in which subjects are asked to guide a group of ground robots with various configurations to arrive at a sequence of randomly assigned targets. A range of neurophysiological and behavioral sensors are used to measure how cognitive states, e.g., cognitive load, behaviors, e.g., gazes, and performances, e.g., success rate, of human performs unfold in real time as the tasks evolve. Through an analysis of changes in gaze and cognitive load, we gain a wider understanding of the mechanisms of task failure; most notably the difficulty of estimating the complete state of the robotic group. The results of this study can help to inform the design of efficient interaction policies which can maximize task effectiveness between humans and robot swarms.

[1]  Pradip M. Jawandhiya,et al.  Review of Unmanned Aircraft System (UAS) , 2013 .

[2]  Glenn F. Wilson,et al.  Augmented Cognition in Human–System Interaction , 2006 .

[3]  W. Klimesch EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[4]  Barbara Linke,et al.  Recognizing Gaze-Motor Behavioral Patterns in Manual Grinding Tasks , 2016 .

[5]  Yanwei Wang,et al.  Fast Discrete Orthonormal Stockwell Transform , 2009, SIAM J. Sci. Comput..

[6]  Hussein A. Abbass,et al.  Augmented Cognition using Real-time EEG-based Adaptive Strategies for Air Traffic Control , 2014 .

[7]  Zhaodan Kong,et al.  Modeling Human Guidance Behavior Based on Patterns in Agent–Environment Interactions , 2013, IEEE Transactions on Human-Machine Systems.

[8]  Antonio Franchi,et al.  Bilateral Teleoperation of Groups of UAVs with Decentralized Connectivity Maintenance , 2011, Robotics: Science and Systems.

[9]  Cláudio T. Silva,et al.  A User Study of Visualization Effectiveness Using EEG and Cognitive Load , 2011, Comput. Graph. Forum.

[10]  Debatri Chatterjee,et al.  Inactive-state recognition from EEG signals and its application in cognitive load computation , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[11]  Katia P. Sycara,et al.  Human Control of Leader-Based Swarms , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[12]  Bin Li,et al.  Classification of Human Gaze in Spatial Guidance and Control , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[13]  Andrew S. Clare,et al.  Modeling the Impact of Operator Trust on Performance in Multiple Robot Control , 2013, AAAI Spring Symposium: Trust and Autonomous Systems.

[14]  Alan Kennedy,et al.  Book Review: Eye Tracking: A Comprehensive Guide to Methods and Measures , 2016, Quarterly journal of experimental psychology.

[15]  Nigel H. Lovell,et al.  Beyond Subjective Self-Rating: EEG Signal Classification of Cognitive Workload , 2015, IEEE Transactions on Autonomous Mental Development.

[16]  Katia P. Sycara,et al.  Human Interaction With Robot Swarms: A Survey , 2016, IEEE Transactions on Human-Machine Systems.

[17]  Mary L. Cummings,et al.  Global vs. local decision support for multiple independent UAV schedule management , 2010, Int. J. Appl. Decis. Sci..

[18]  Michael A. Goodrich,et al.  Human-Swarm Interactions Based on Managing Attractors , 2014, 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[19]  Katia P. Sycara,et al.  Human control of robot swarms with dynamic leaders , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Katia Sycara,et al.  Human-swarm interaction , 2013, HRI 2013.

[21]  Michael A. Goodrich,et al.  Scalable Human Interaction with Robotic Swarms , 2013 .

[22]  Zhaodan Kong,et al.  Systems view on spatial planning and perception based on invariants in agent-environment dynamics , 2015, Front. Neurosci..