Influence of human-computer interface elements on performance of teleoperated mobile robot

Mobile robots are becoming ubiquitous, with applications which usually include a degree of autonomy. However, due to uncertain and dynamic nature of operational environment, algorithms for autonomous operation might fail. In order to assist the robot, the human operator might need to take control over the robot from remote location. In order to efficiently and safely teleoperate the robot, the operator has to have high degree of situational awareness. This can be achieved with appropriate human-computer interface (HCI), so that the remote environment model constructed with sensor data is presented at appropriate time, and that robot commands can be issued intuitively and easily. In the research, influence of HCI elements on performance of teleoperated mobile robot was studied for several tasks and with several HCI setups. The user study was performed, in which accuracy and speed of completion of given tasks were measured on a real robot. Statistical analysis was performed in order to identify possible setup dependencies. It showed that, in majority of analysed cases and based on introduced metrics, there is no significant difference between the setups, and between the visual control and teleoperation. Finally, conclusions were drawn with emphasis on benefits of information technology in particular case.

[1]  Andreas Birk,et al.  The IUB Rugbot: an intelligent, rugged mobile robot for search and rescue operations , 2006 .

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

[3]  Alan J. Dix Human-Computer Interaction , 2018, Encyclopedia of Database Systems.

[4]  Michael A. Goodrich,et al.  Comparing the usefulness of video and map information in navigation tasks , 2006, HRI '06.

[5]  Terrence Fong,et al.  Novel interfaces for remote driving: gesture, haptic, and PDA , 2001, SPIE Optics East.

[6]  Stephen A. McGuire,et al.  Introductory Statistics , 2007, Technometrics.

[7]  Dawn M. Tilbury,et al.  Driver Modeling for Teleoperation with Time Delay , 2014 .

[8]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[9]  David B. Kaber,et al.  Situation awareness and attention allocation measures for quantifying telepresence experiences in teleoperation , 2004 .

[10]  Weihua Li,et al.  Stable kinematic teleoperation of wheeled mobile robots with slippage using time-domain passivity control , 2016 .

[11]  Maria Bualat,et al.  Virtual Reality Interfaces for Visualization and Control of Remote Vehicles , 2001, Auton. Robots.

[12]  Michael A. Goodrich,et al.  Ecological Interfaces for Improving Mobile Robot Teleoperation , 2007, IEEE Transactions on Robotics.

[13]  Serengul Smith-Atakan The FastTrack to Human-Computer Interaction , 2006 .

[14]  Jaroslaw Jankowski,et al.  Usability Evaluation of VR Interface for Mobile Robot Teleoperation , 2015, Int. J. Hum. Comput. Interact..

[15]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

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

[17]  Josip Music,et al.  Cleaning up smart cities — Localization of semi-autonomous floor scrubber , 2016, 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech).