Abstract
The teleoperation of robot manipulators over the internet suffers from variable delays in the communications. Here we address a tele-assistance scenario, where a remote operator assists a disabled or elderly user on daily life tasks. Our behavioral approach uses local environment information from robot sensing to help enable faster execution for a given movement tolerance. This is achieved through a controller that automatically slows the operator down before having collisions, using a set of distributed proximity sensors. The controller is made to gradually increase the assistance in situations similar to those where ollisions have occurred in the past, thus adapting to the given operator, robot and task-set. Two controlled virtual experiments for tele-assistance with a 5 DOF manipulator were performed, with 300 ms and 600 ms mean variable round-trip delays. The results showed significant improvements in the median times of 12.6% and 16.5%, respectively. Improvements in the subjective workload were also seen with the controller. A first implementation on a physical robot manipulator is described.
References
[1] A. Jardón, A. Giménez, R. Correal, R. Cabas, S.Martínez, C. Balaguer, A portable light-weight climbing robot for personal assistance applications, Industrial Robot: An International Journal, 33 (2006), no. 4, 303-307 Search in Google Scholar
[2] D.J. Kim, et al., How Autonomy Impacts Performance and Satisfaction: Results From a StudyWith Spinal Cord Injured Subjects Using an Assistive Robot, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 42 (2012), no. 1, 2-14 Search in Google Scholar
[3] H. Jiang, T. Zhang, J.P. Wachs, B.S. Duerstock, Autonomous Performance of Multistep Activities with a Wheelchair Mounted Robotic Manipulator Using Body Dependent Positioning, Workshop on Assistive Robotics for Individuals with Disabilities: HRI Issues and Beyond, IEEE/RSJ Int. Conf. on Intell. Robots and Systems, 2014 Search in Google Scholar
[4] D.J. Kim, Z. Wang, N. Paperno, A. Behal, System Design and Implementation of UCF-MANUS - An Intelligent Assistive Robotic Manipulator, IEEE/ASME Transactions on Mechatronics, 19 (2014), no. 1, 225-237 Search in Google Scholar
[5] M.F. Stoelen, V.F. Tejada, A. Jardón, F. Bonsignorio and C. Balaguer, Benchmarking Shared Control for Assistive Manipulators: From Controllability to the Speed-Accuracy Trade-Off, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2012, Vilamoura, Portugal), IEEE, 2012, 4386-4391 10.1109/IROS.2012.6385847Search in Google Scholar
[6] M.F. Stoelen, V.F. Tejada, J.G. Victores, A. Jardón, F. Bonsignorio and C. Balaguer, Adaptive Collision-Limitation Behavior for an Assistive Manipulator, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2013, Tokyo, Japan), IEEE, 2013, 1143-1148 10.1109/IROS.2013.6696494Search in Google Scholar
[7] T.L. Chen et al., Robots for humanity: using assistive robotics to empower people with disabilities, IEEE Robot. Automat. Mag., 20 (2013), no. 1, 30-39 Search in Google Scholar
[8] C. Passenberg, A. Peer, M. Buss, A survey of environment-, operator-, and task-adapted controllers for teleoperation systems, Mechatronics, 20 (2010), no. 7, 787-801 Search in Google Scholar
[9] P.M. Fitts, The Information Capacity of the human Motor System in Controlling the Amplitude of Movement, Journal of Experimental Psychology, 47 (1954), 381-391 10.1037/h0055392Search in Google Scholar
[10] J.T. Dennerlein, D.B. Martin, C. Hasser, Force-feedback improves performance for steering and combined steeringtargeting tasks, Proceedings of the SIGCHI conference on Human factors in computing systems - CHI’00 (2000, The Hague, The Netherlands), vol. 2, no. 1, 423-429 10.1145/332040.332469Search in Google Scholar
[11] J.C. Lane, C.R. Carignan, D.L. Akin, Advanced Operator Interface Design for Complex Space Telerobots, Autonomous Robots, 11 (2001), 49-58 10.1023/A:1011256128651Search in Google Scholar
[12] J. Davis, C. Smyth, K. McDowell, The Effects of Time Lag on Driving Performance and a Possible Mitigation, IEEE Transactions on Robotics, 6 (2010), no. 3, 590-593 Search in Google Scholar
[13] C. Basdogan, M.A. Srinivasan, Haptic Rendering in Virtual Environments. In K. Stanney (Ed.), Virtual Environments Handbook, Lawrence Erlbaum Associates, 2001, 117-134 Search in Google Scholar
[14] Z. Xiu, A. Kitagawa, H. Tsukagoshi, C. Liu, M. Ido, P.Wu, Internetbased Tele-rehabilitation System - Bilateral Tele-control with Variable Time Delay, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (2006, Beijing, China), IEEE, 2006, 5208-5213 10.1109/IROS.2006.281659Search in Google Scholar
[15] C.R. Carignan, H.I. Krebs, Telerehabilitation robotics: Bright lights, big future?, Journal of Rehabilitation Research & Development, 43 (2006), no. 5, 695-710 Search in Google Scholar
[16] E.J. Rodríguez-Seda, D. Lee, M.W. Spong, Experimental Comparison Study of Control Architectures for Bilateral Teleoperators, IEEE Transactions on Robotics, 25 (2009), no. 6, 1304-1318 Search in Google Scholar
[17] E. Demeester, A. Huntemann, D. Vanhooydonck, G. Vanacker, H. Brussel, M. Nuttin, User-adapted plan recognition and user-adapted shared control: A bayesian approach to semiautonomous wheelchair driving, Journal of Autonomous Robots, 24 (2008), 193-211 10.1007/s10514-007-9064-5Search in Google Scholar
[18] T. Carlson, Y. Demiris, Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 42 (2012), no. 3, 876-888 Search in Google Scholar
[19] S.E. Everett, R.V. Dubey, Human-Machine Cooperative Telerobotics Using Uncertain Sensor or Model Data, Proceedings of IEEE International Conference on Robotics and Automation (1998, Leuven, Belgium), IEEE, 1998, 1615-1622 Search in Google Scholar
[20] D.A. Abbink, M. Mulder, E.R. Boer, Haptic shared control: smoothly shifting control authority?, Cognition, Technology & Work, 14 (2012), 19-28 10.1007/s10111-011-0192-5Search in Google Scholar
[21] P.F.M.J. Verschure, B.J.A. Krose, R. Pfeifer, Distributed adaptive control: The self-organization of structured behavior, Robotics and Autonomous Systems, 9 (1993), no. 3, 181-196 Search in Google Scholar
[22] P.F.M.J. Verschure, T. Voegtlin, R.J. Douglas, Environmentally mediated synergy between perception and behaviour in mobile robots, Nature, 425 (2003), no. 6958, 620-624 Search in Google Scholar
[23] R. Pfeifer, J. Bongard, How the body shapes the way we think, MIT Press, Cambridge, MA, USA, 2006 10.7551/mitpress/3585.001.0001Search in Google Scholar
[24] I. Pavlov, Conditional Reflexes, Dover Publications, New York, NY, USA, 1927/1960 Search in Google Scholar
[25] H. Yousef, M. Boukallel, K. Althoefer, Tactile sensing for dexterous in-hand manipulation in robotics - A review, Sensors and Actuators A: Physical, 167 (2011), no. 2, 171-187 Search in Google Scholar
[26] E. Cheung, V.J. Lumelsky, Proximity sensing in robot manipulator motion planning: system and implementation issues, IEEE Transactions on Robotics and Automation, 5 (1989), no. 5, 740- 751 Search in Google Scholar
[27] K. Hsiao, P. Nangeroni, M. Huber, A. Saxena, A.Y. Ng, Reactive grasping using optical proximity sensors, Proceedings of IEEE International Conference on Robotics and Automation (2009, Kobe, Japan), IEEE, 2009, 2098-2105 10.1109/ROBOT.2009.5152849Search in Google Scholar
[28] S.G. Hart, L.E. Staveland, Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research, Advances in psychology, 52 (1988), 139-183 10.1016/S0166-4115(08)62386-9Search in Google Scholar
[29] A. Kiselev, A. Loutfi, Using a Mental Workload Index as a Measure of Usability of a User Interface for Social Robotic Telepresence, in Proceedings of the Ro-Man 2012 Workshop in Social Robotic Telepresence, 2012, 3-6 Search in Google Scholar
[30] R. Diankov, Automated Construction of Robotics Manipulation Programs, PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA, 2010 Search in Google Scholar
[31] J.Y. Park, P.H. Chang, J.Y. Yang, Task-oriented design of robot kinematics using the Grid Method, Advanced Robotics, 17 (2003), no. 9, 879-907 Search in Google Scholar
[32] A. Jardón, M.F. Stoelen, F. Bonsignorio, C. Balaguer, Task- Oriented Kinematic Design of a Symmetric Assistive Climbing Robot, IEEE Transactions on Robotics, 27 (2011), no. 6, 1132-1137 Search in Google Scholar
[33] J.R. Smith et al., Electric field imaging pretouch for robotic graspers, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (2007, San Diego, CA, USA), IEEE, 2007, 676-683 10.1109/IROS.2007.4399609Search in Google Scholar
[34] S.E. Navarro et al., Methods for safe human-robot-interaction using capacitive tactile proximity sensors, Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2013, Tokyo, Japan), IEEE, 2013, 1149-1154 10.1109/IROS.2013.6696495Search in Google Scholar
[35] G. Cannata, M. Maggiali, G. Metta, G. Sandini, An Embedded Artificial Skin for Humanoid Robots, Proc. of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (2008, Seoul, Korea), IEEE, 2008. 10.1109/MFI.2008.4648033Search in Google Scholar
[36] R. Pfeifer, M. Lungarella, F. Iida, Self-organization, embodiment, and biologically inspired robotics, Science, 318 (2007), no. 5853, 1088-1093 Search in Google Scholar
© 2016 Martin F. Stoelen et al.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
