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Teleoperation grasp assistance using infra-red sensor array

Published online by Cambridge University Press:  24 March 2014

Nutan Chen ,
Keng Peng Tee  and
Chee-Meng Chew
Nutan Chen*
Affiliation:
Department of Mechanical Engineering, National University of Singapore, Singapore, 117576 SG Faculty for Informatics, Technical University Munich, Munich, Germany, 80333 DE
Keng Peng Tee
Affiliation:
Institute for Infocomm Research, A*STAR, Singapore, 138632 SG
Chee-Meng Chew
Affiliation:
Department of Mechanical Engineering, National University of Singapore, Singapore, 117576 SG
*
*Corresponding author. E-mail: nutan@in.tum.de
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Summary

Teleoperated grasping requires the abilities to follow the intended trajectory from the user and autonomously search for a suitable pre-grasp pose relative to the object of interest. Challenges include dealing with uncertainty due to the noise of the teleoperator, human elements and calibration errors in the sensors. To address these challenges, an effective and robust algorithm is introduced to assist grasping during teleoperation. Although without premature object contact or regrasping strategies, the algorithm enables the robot to perform online adjustments to reach a pre-grasp pose before final grasping. We use three infrared (IR) sensors that are mounted on the robot hand, and design an algorithm that controls the robot hand to grasp objects using the information from the sensors' readings and the interface component. Finally, a series of experiments demonstrate that the system is robust when grasping a wide range of objects and tracking slow-moving mobile objects. Empirical data from a five-subject user study allows us to tune the relative contributions from the IR sensors and the interface component so as to achieve a balance of grasp assistance and teleoperation.

Keywords

Shared controlGraspingTeleoperationHuman–robot interactionIR sensor
Type
Articles
Information
Robotica , Volume 33 , Issue 4 , May 2015 , pp. 986 - 1002
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Li, L., Cox, B., Diftler, M., Shelton, S. and Rogers, B.. “Development of a Telepresence Controlled Ambidextrous Robot for Space Applications,” In: Proceedings of the 1996 IEEE International Conference on Robotics and Automation, vol. 1 (Apr. 1996) pp. 58–63.Google Scholar
2. Lee, D., Martinez-Palafox, O. and Spong, M.. “Bilateral Teleoperation of Multiple Cooperative Robots Over Delayed Communication Networks: Application,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005) (2005) pp. 366–371.Google Scholar
3. Ambrose, R., Aldridge, H., Askew, R., Burridge, R., Bluethmann, W., Diftler, M., Lovchik, C., Magruder, D. and Rehnmark, F.. “Robonaut: NASA's space humanoid,” IEEE Intell. Syst. Appl. 15 (4), 57–63 (2000).CrossRefGoogle Scholar
4. Griffin, W. B., Provancher, W. R. and Cutkosky, M. R.. “Feedback Strategies for Shared Control in Dexterous Telemanipulation,” In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 3 (Oct. 2003) pp. 2791–2796.Google Scholar
5. Fischer, M., van der Smagt, P. and Hirzinger, G.. “Learning Techniques in a Dataglove-Based Telemanipulation System for the DLR Hand,” In: Proceedings of the 1998 IEEE International Conference on Robotics and Automation, vol. 2 (May 1998) pp. 1603–1608.Google Scholar
6. Popescu, V., Burdea, G., Bouzit, M. and Hentz, V.. “A virtual-reality-based telerehabilitation system with force feedback,” IEEE Trans. Inf. Technol. Biomed. 4 (1), 45–51 (2000).CrossRefGoogle ScholarPubMed
7. Lei, C., Ggo, C. and Dai, J. S.. “Kinematic Mapping and Calibration of the Thumb Motions for Teleoperating a Humanoid Robot Hand,” ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2011) pp. 1139–1147.Google Scholar
8. Popović, M., Kraft, D., Bodenhagen, L., Başeski, E., Pugeault, N., Kragic, D., Asfour, T. and Krüger, N., “A strategy for grasping unknown objects based on co-planarity and colour information,” Robot. Auton. Syst. 58, 551–565 (2010).CrossRefGoogle Scholar
9. Saxena, A., Wong, L. L. S. and Ng, A. Y.. “Learning grasp strategies with partial shape information,” AAAI 3 (2), 1491–1494 (2008).Google Scholar
10. Wang, B., Jiang, L., LI, J. and Cai, H.. “Grasping Unknown Objects Based on 3D Model Reconstruction,” Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2005) pp. 461–466.Google Scholar
11. Kemp, C. C., Anderson, C. D., Trevor, A. J. and Xu, Z.. “A Point-and-Click Interface for the Real World: Laser Designation of Objects for Mobile Manipulation,” Proceedings of the 3rd ACM/IEEE International Conference (2008) pp. 241–248.Google Scholar
12. Jain, A. and Kemp, C. C., “El-e: An assistive mobile manipulator that autonomously fetches objects from flat surfaces,” Auton. Robots 28 (1), 45–64 (2009).CrossRefGoogle Scholar
13. Petrovskaya, A., Khatib, O., Thrun, S. and Ng, A., “Bayesian Estimation for Autonomous Object Manipulation Based on Tactile Sensors,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA) (2006) pp. 707–714.Google Scholar
14. Hsiao, K., Chitta, S., Ciocarlie, M. and Jones, E., “Contact-Reactive Grasping of Objects with Partial Shape Information,” 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 1228–1235.Google Scholar
15. Leeper, A., Hsiao, K., Chu, E. and Salisbury, J. K., “Using Near-Field Stereo Vision for Robotic Grasping in Cluttered Environments,” In: Experimental Robotics (Springer Tracts in Advanced Robotics), (Khatib, O., Kumar, V. and Sukhatme, G., eds.), vol. 79 (Springer Berlin Heidelberg, 2014) pp. 253–267.CrossRefGoogle Scholar
16. Smith, J., Garcia, E., Wistort, R. and Krishnamoorthy, G.. “Electric Field Imaging Pretouch for Robotic Graspers,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'07) (2007) pp. 676–683.Google Scholar
17. Wistort, R. and Smith, J.. “Electric Field Servoing for Robotic Manipulation,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'08) (2008) pp. 494–499.Google Scholar
18. Jiang, L.-T. and Smith, J.. “Seashell Effect Pretouch Sensing for Robotic Grasping,” 2012 IEEE International Conference on Robotics and Automation (ICRA) (2012) pp. 2851–2858.Google Scholar
19. Hsiao, K., Nangeroni, P., Huber, M., Saxena, A. and Ng, A. Y.. “Reactive Grasping Using Optical Proximity Sensors,” IEEE International Conference on Robotics and Automation (ICRA'09) (2009) pp. 2098–2105.Google Scholar
20. Kim, H., Biggs, J., Schloerb, W., Carmena, M., Lebedev, M., Nicolelis, M. and Srinivasan, M., “Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces,” IEEE Trans. Biomed. Eng. 53 (6), 1164–1173 (2006).CrossRefGoogle ScholarPubMed
21. Teichmann, M. and Mishra, B.. “Reactive robotics I: Reactive grasping with a modified gripper and multifingered hands,” Int. J. Robot. Res. 19 (7), 697–708 (2000).CrossRefGoogle Scholar
22. Rimon, E. and Koditschek, D.. “Exact robot navigation using artificial potential functions,” IEEE Trans. Robot. Autom. 8 (5), 501–518 (1992).CrossRefGoogle Scholar
23. Chen, N., Chew, C.-M., Tee, K. P. and Han, B. S.. “Human-Aided Robotic Grasping,” 21st IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN, 2012) Paris, France (Sep. 9–13, 2012) pp. 75–80.Google Scholar