Visual-Vestibular Feedback for Enhanced Situational Awareness in Teleoperation of UAVs

This paper presents a novel concept for improving the situational awareness of a ground operator in remote control of a Unmanned Arial Vehicle (UAV). To this end, we propose to integrate vestibular feedback with the usual visual feedback obtained from a UAV onboard camera. We use our motion platform, the CyberMotion simulator, so as to reproduce online the desired motion cues. We test this architecture by flying a small-scale quadcopter and run a detailed performance evaluation on 12 test subjects. We then discuss the results in terms of possible benefits for facilitating the remote control task.

[1]  Max Mulder,et al.  The Effect of Simulator Motion on Pilot Control Behaviour for Agile and Inert Helicopter Dynamics , 2009 .

[2]  Heinrich H. Bülthoff,et al.  A Bayesian model of the disambiguation of gravitoinertial force by visual cues , 2007, Experimental Brain Research.

[3]  Avi Parush.  7. Human Errors in UAV Takeoff and Landing: Theoretical Account and Practical Implications , 2006 .

[4]  B J Hess,et al.  Computation of Inertial Motion: Neural Strategies to Resolve Ambiguous Otolith Information , 1999, The Journal of Neuroscience.

[5]  Jean-Guy Fontaine,et al.  Description and tests of a multisensorial driving interface for vehicle teleoperation , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[6]  Carlo Masone,et al.  A novel framework for closed-loop robotic motion simulation - part I: Inverse kinematics design , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  M. O. Tokhi,et al.  Climbing and Walking Robots - Proceedings of the 8th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2005, London, UK, September 13-15, 2005 , 2006, CLAWAR.

[8]  Paul Y. Oh,et al.  Development of an Unmanned Aerial Vehicle Piloting System with Integrated Motion Cueing for Training and Pilot Evaluation , 2009, J. Intell. Robotic Syst..

[9]  R. Mclean,et al.  A Unified Approach to Mixed Linear Models , 1991 .

[10]  H. Mittelstaedt.  A new solution to the problem of the subjective vertical , 1983, Naturwissenschaften.

[11]  Nancy J. Cooke,et al.  Advances in Human Performance and Cognitive Engineering Research , 2002 .

[12]  Horst Mittelstaedt,et al.  Idiothetic navigation in humans: estimation of path length , 2001, Experimental Brain Research.

[13]  Dora E. Angelaki,et al.  Neurons compute internal models of the physical laws of motion , 2004, Nature.

[14]  Kimon P. Valavanis,et al.  Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy , 2007 .

[15]  Climbing and Walking Robots (CLAWAR 2001) , .

[16]  D M Merfeld,et al.  Humans use internal models to estimate gravity and linear acceleration , 1999, Nature.

[17]  Jean Scholtz,et al.  Awareness in human-robot interactions , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[18]  Heinrich H. Bülthoff,et al.  Learning System Dynamics: Transfer of Tranining in a Helicopter Hover Simulator , 2008 .

[19]  Michael Kerger,et al.  MPI Motion Simulator: Development and Analysis of a Novel Motion Simulator , 2007 .

[20]  Carlo Masone,et al.  A novel framework for closed-loop robotic motion simulation - part II: Motion cueing design and experimental validation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[21]  Harald Teufel,et al.  A Roll-Lateral Helicopter Side-Step Maneuver on the MPI Motion Simulator , 2008 .

[22]  Mario Maza,et al.  Vehicle Teleoperation with a Multisensory Driving Interface , 2005 .