The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Human–Robot Collaboration Based on Motion Intention Estimation
Open accessAbstract
In this paper, adaptive impedance control is proposed for a robot collaborating with a human partner, in the presence of unknown motion intention of the human partner and unknown robot dynamics. Human motion intention is defined as the desired trajectory in the limb model of the human partner, which is extremely difficult to obtain considering the nonlinear and time-varying property of the limb model. Neural networks are employed to cope with this problem, based on which an online estimation method is developed. The estimated motion intention is integrated into the developed adaptive impedance control, which makes the robot follow a given target impedance model. Under the proposed method, the robot is able to actively collaborate with its human partner, which is verified through experiment studies.
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Cites
Impedance Control: An Approach to Manipulation: Part I—Theory
Hogan, N. (1985) · Journal of Dynamic Systems, Measurement, and Control · doi:10.1115/1.3140702 · Cited by 3,673
Neural Network Control Of Robot Manipulators And Non-Linear Systems
Lewis, F. W., Jagannathan, S., & Yesildirak, A. (2020) · CRC Press eBooks · doi:10.1201/9781003062714 · Cited by 1,867
Adaptive Neural Network Control of Robotic Manipulators
Ge, S. S., Lee, T. H., & Harris, C. J. (1998) · World Scientific Series in Robotics and Intelligent Systems · doi:10.1142/3774 · Cited by 618
Self-citationInertial Properties in Robotic Manipulation: An Object-Level Framework
Khatib, O. (1995) · The International Journal of Robotics Research · doi:10.1177/027836499501400103 · Cited by 542
Human hand impedance characteristics during maintained posture
Tsuji, T., Morasso, P. G., Goto, K., & Ito, K. (1995) · Biological Cybernetics · doi:10.1007/bf00199890 · Cited by 469
Force Tracking in Impedance Control
Seraji, H., & Colbaugh, R. (1997) · The International Journal of Robotics Research · doi:10.1177/027836499701600107 · Cited by 301
Complementary Stability and Loop Shaping for Improved Human–Robot Interaction
Buerger, S. P., & Hogan, N. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.892229 · Cited by 269
Human-Intent Detection and Physically Interactive Control of a Robot Without Force Sensors
Erden, M. S., & Tomiyama, T. (2010) · IEEE Transactions on Robotics · doi:10.1109/tro.2010.2040202 · Cited by 136
Quantization of human motions and learning of accurate movements
Burdet, E., & Milner, T. E. (1998) · Biological Cybernetics · doi:10.1007/s004220050435 · Cited by 135
An HMM approach to realistic haptic human-robot interaction
Wang, Z., Peer, A., & BUSS, M. J. (2009) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2009.4810835 · Cited by 130
Cited by
Cobot programming for collaborative industrial tasks: An overview
El Zaatari, S., Marei, M., Li, W., & Usman, Z. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.03.003 · Cited by 470
Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints
He, W., Huang, H., & Ge, S. S. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2711961 · Cited by 361
Self-citationHuman motion prediction for human-robot collaboration
Liu, H., & Wang, L. (2017) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2017.04.009 · Cited by 193
Metadata
- Authors
- Li, Y., & Ge, S. S.
- Venue
- IEEE/ASME Transactions on Mechatronics
- Published
- 2014-06
- Volume
- 19
- Issue
- 3
- Pages
- 1007-1014
- ISSN
- 1083-4435 (ISSN-L)
- 1941-014X
- OpenAlex
- W2043536379
- Cited by
- 320
- References
- 18
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
