Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction

Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation.

[1]  Bart Adams,et al.  Astute: Increased Situational Awareness through Proactive Decision Support and Adaptive Map-Centric User Interfaces , 2013, 2013 European Intelligence and Security Informatics Conference.

[2]  Jeffrey T. Hansberger,et al.  Development of the Next Generation of Adaptive Interfaces , 2015 .

[3]  Antonio Barrientos,et al.  Determining mission evolution through UAV telemetry by using decision trees , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[4]  A. O. Dick,et al.  Operator Workload: Comprehensive Review and Evaluation of Operator Workload Methodologies , 1989 .

[5]  Jessie Y. C. Chen,et al.  Supervisory Control of Multiple Robots: Human-Performance Issues and User-Interface Design , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Chang S Nam,et al.  A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management. , 2017, Applied ergonomics.

[7]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[8]  Suk-Ju Kang,et al.  Photosensor-Based Latency Measurement System for Head-Mounted Displays , 2017, Sensors.

[9]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[10]  Erwin Prassler,et al.  KUKA youBot - a mobile manipulator for research and education , 2011, 2011 IEEE International Conference on Robotics and Automation.

[11]  Tina Mioch,et al.  Establishing human situation awareness using a multi-modal operator control unit in an urban search & rescue human-robot team , 2011, 2011 RO-MAN.

[12]  Erik Fagerholt,et al.  Beyond the HUD - User Interfaces for Increased Player Immersion in FPS Games , 2009 .

[13]  Michael A. Goodrich,et al.  Comparing Situation Awareness for Two Unmanned Aerial Vehicle Human Interface Approaches , 2006 .

[14]  Luca Maria Gambardella,et al.  Human Control of UAVs using Face Pose Estimates and Hand Gestures , 2014, 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[15]  W. A. Olson,et al.  Autonomy based human-vehicle interface standards for remotely operated aircraft , 2001, 20th DASC. 20th Digital Avionics Systems Conference (Cat. No.01CH37219).

[16]  Antonio Barrientos,et al.  Using Process Mining to Model Multi-UAV Missions through the Experience , 2017, IEEE Intelligent Systems.

[17]  R. Murphy,et al.  Up from the Rubble: Lessons Learned about HRI from Search and Rescue , 2005 .

[18]  Ben Horan,et al.  Building a Relationship between Robot Characteristics and Teleoperation User Interfaces , 2017, Sensors.

[19]  Younho Seong,et al.  Evaluation of human–agent user interfaces in multi-agent systems , 2009 .

[20]  Ehud Sharlin,et al.  Flying Frustum: A Spatial Interface for Enhancing Human-UAV Awareness , 2015, HAI.

[21]  Jan Faigl,et al.  AR-Drone as a Platform for Robotic Research and Education , 2011, Eurobot Conference.

[22]  Renato Zaccaria,et al.  Visual feedback with multiple cameras in a UAVs Human-Swarm Interface , 2016, Robotics Auton. Syst..

[23]  Li Li,et al.  The Accuracy and Precision of Position and Orientation Tracking in the HTC Vive Virtual Reality System for Scientific Research , 2017, i-Perception.

[24]  Birsen Donmez,et al.  Modeling Workload Impact in Multiple Unmanned Vehicle Supervisory Control , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[25]  Narciso García,et al.  Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators , 2017, Sensors.

[26]  Mary L. Cummings,et al.  Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control , 2013, Interact. Comput..

[27]  Corinna E. Lathan,et al.  The Effects of Operator Spatial Perception and Sensory Feedback on Human-Robot Teleoperation Performance , 2002, Presence: Teleoperators & Virtual Environments.

[28]  Andreas Lüdtke,et al.  SA-Tracer: A tool for assessment of UAV swarm operator SA during mission execution , 2013, 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[29]  Miguel A. Olivares-Méndez,et al.  A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors , 2016, 2016 IEEE Conference on Control Applications (CCA).

[30]  Narciso García Santos,et al.  New generation of human machine interfaces for controlling UAV through depth based gesture recognition , 2014 .

[31]  Jessie Y. C. Chen,et al.  Human Performance Issues and User Interface Design for Teleoperated Robots , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[32]  Mica R. Endsley,et al.  Situation awareness global assessment technique (SAGAT) , 1988, Proceedings of the IEEE 1988 National Aerospace and Electronics Conference.

[33]  A. Ollero,et al.  Immersive displays for building spatial knowledge in multi-UAV operations , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[34]  Sandra G. Hart,et al.  Nasa-Task Load Index (NASA-TLX); 20 Years Later , 2006 .

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

[36]  Max Mulder,et al.  An Ecological Approach to the Supervisory Control of UAV Swarms , 2014 .

[37]  Lei Shi,et al.  Two-Stage Road Terrain Identification Approach for Land Vehicles Using Feature-Based and Markov Random Field Algorithm , 2018, IEEE Intelligent Systems.

[38]  Hasan Ayaz,et al.  Optical Brain Imaging to Enhance UAV Operator Training, Evaluation, and Interface Development , 2011, J. Intell. Robotic Syst..

[39]  S. Kavitha,et al.  Development of automatic speech recognition system for voice activated Ground Control system , 2015, 2015 International Conference on Trends in Automation, Communications and Computing Technology (I-TACT-15).

[40]  Mary L. Cummings,et al.  Automation Architecture for Single Operator, Multiple UAV Command and Control, , 2007 .

[41]  Katsunori Matsuoka,et al.  Autonomic responses during motion sickness induced by virtual reality. , 2007, Auris, nasus, larynx.

[42]  E. C. Haas,et al.  Multimodal controls for soldier/swarm interaction , 2011, 2011 RO-MAN.

[43]  Julia Frankfurter,et al.  Mental Workload Its Theory And Measurement , 2016 .

[44]  Michael Lewis,et al.  Towards human control of robot swarms , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[45]  Domenico Prattichizzo,et al.  Cooperative human-robot haptic navigation , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[46]  MengChu Zhou,et al.  Optimizing Operator–Agent Interaction in Intelligent Adaptive Interface Design: A Conceptual Framework , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[47]  Greg Mori,et al.  Integrating multi-modal interfaces to command UAVs , 2014, HRI.

[48]  David Sanz Muñoz Cognitive risk perception system for obstacle avoidance in outdoor mUAV missions , 2015 .

[49]  Jean Scholtz,et al.  Evaluation of human-robot interaction awareness in search and rescue , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[50]  Miguel A. Olivares-Méndez,et al.  Analyzing and improving multi-robot missions by using process mining , 2018, Auton. Robots.