Path-Constrained Haptic Motion Guidance via Adaptive Phase-Based Admittance Control, Publisher: IEEE
Erfan Shahriari; Petr Svarny; Seyed Ali Baradaran Birjandi; Matej Hoffmann; Sami Haddadin
https://ieeexplore.ieee.org/document/10814694

Referebce

Y.-H. Hsieh, Y.-C. Huang, K.-Y. Young, C.-H. Ko, and S. K. Agrawal, “Motion guidance for a passive robot walking helper via user's applied hand forces,” IEEE Trans. Hum.- Mach. Syst., vol. 46, no. 6, pp. 869–881, Dec. 2016.
X. Lamy, F. Colledani, F. Geffard, Y. Measson, and G. Morel, “Achieving efficient and stable comanipulation through adaptation to changes in human arm impedance,” in Proc. IEEE Int. Conf. Robot. Autom., 2009, pp. 265–271.
Z. Li, B. Huang, Z. Ye, M. Deng, and C. Yang, “Physical human–robot interaction of a robotic exoskeleton by admittance control,” IEEE Trans. Ind. Electron., vol. 65, no. 12, pp. 9614–9624, Dec. 2018.
X. Wu, Z. Li, Z. Kan, and H. Gao, “Reference trajectory reshaping optimization and control of robotic exoskeletons for human-robot co-manipulation,” IEEE Trans. Cybern., vol. 50, no. 8, pp. 3740–3751, Aug. 2020.
H. Seraji, “Adaptive admittance control: An approach to explicit force control in compliant motion,” in Proc. IEEE Int. Conf. Robot. Autom., 1994, pp. 2705–2712.
A. Q. Keemink, H. van der Kooij, and A. H. Stienen, “Admittance control for physical human–robot interaction,” Int. J. Robot. Res., vol. 37, no. 11, pp. 1421–1444, 2018.
D. Reyes-Uquillas and T. Hsiao, “Safe and intuitive manual guidance of a robot manipulator using adaptive admittance control towards robot agility,” Robot. Comput.-Integr. Manuf., vol. 70, 2021, Art. no. 102127.
K. Haninger, M. Radke, A. Vick, and J. Krüger, “Towards high-payload admittance control for manual guidance with environmental contact,” IEEE Robot. Autom. Lett., vol. 7, no. 2, pp. 4275–4282, 2022.
D. Bazzi, G. Priora, A. M. Zanchettin, and P. Rocco, “RRT* and goal-driven variable admittance control for obstacle avoidance in manual guidance applications,” IEEE Robot. Autom. Lett., vol. 7, no. 2, pp. 1920–1927, 2022.
E. Mariotti, E. Magrini, and A. De Luca, “Admittance control for human-robot interaction using an industrial robot equipped with a F/T sensor,” in Proc. Int. Conf. Robot. Autom., 2019, pp. 6130–6136.
M. Mujica, M. Crespo, M. Benoussaad, S. Junco, and J.-Y. Fourquet, “Robust variable admittance control for human–robot co-manipulation of objects with unknown load,” Robot. Comput.-Integr. Manuf., vol. 79, 2023, Art. no. 102408.
L. B. Rosenberg, “The use of virtual fixtures as perceptual overlays to enhance operator performance in remote environments,” Wright-Patterson AFB, OH, USA, Tech. Rep. AL/CF-TR-1994-0089, 1994.
P. Marayong, M. Li, A. M. Okamura, and G. D. Hager, “Spatial motion constraints: Theory and demonstrations for robot guidance using virtual fixtures,” in Proc. IEEE Int. Conf. Robot. Autom., 2003, vol. 2, pp. 1954–1959.
C. Sousa, R. Cortesao, and P. Queirós, “Compliant comanipulation control for medical robotics,” in Proc. IEEE 2nd Conf. Hum. Syst. Interact., 2009, pp. 265–271.
V. Duchaine and C. M. Gosselin, “General model of human-robot cooperation using a novel velocity based variable impedance control,” in Proc. 2nd Joint EuroHaptics Conf. Symp. Haptic Interfaces Virtual Environ. Teleoperator Syst., 2007, pp. 446–451.
L. D. Joly and C. Andriot, “Imposing motion constraints to a force reflecting telerobot through real-time simulation of a virtual mechanism,” in Proc. IEEE Int. Conf. Robot. Autom, 1995, vol. 1, pp. 357–362.
S. S. Restrepo, G. Raiola, P. Chevalier, X. Lamy, and D. Sidobre, “Iterative virtual guides programming for human-robot comanipulation,” in Proc. IEEE Int. Conf. Adv. Intell. Mechatron., 2017, pp. 219–226.
G. Raiola, “Co-manipulation with a library of virtual guiding fixtures,” Auton. Robots, vol. 42, pp. 1037–1051, 2018.
B. Nemec, K. Yasuda, and A. Ude, “A virtual mechanism approach for exploiting functional redundancy in finishing operations,” IEEE Trans. Autom. Sci. Eng., vol. 18, no. 4, pp. 2048–2060, Oct. 2020.
K. Kant and S. W. Zucker, “Toward efficient trajectory planning: The path-velocity decomposition,” Int. J. Robot. Res., vol. 5, no. 3, pp. 72–89, 1986.
A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: Learning attractor models for motor behaviors,” Neural Comput., vol. 25, no. 2, pp. 328–373, 2013.
B. Sharma, B. M. Pillai, and J. Suthakorn, “Physical human-robot interaction (pHRI) through admittance control of dynamic movement primitives in sit-to-stand assistance robot,” in Proc. IEEE 15th Int. Conf. Hum. Syst. Interact., 2022, pp. 1–6.
R. J. Escarabajal, J. L. Pulloquinga, Á. Valera, V. Mata, M. Vallés, and F. J. Castillo-García, “Combined admittance control with type II singularity evasion for parallel robots using dynamic movement primitives,” IEEE Trans. Robot., vol. 39, no. 3, pp. 2224–2239, Jun. 2023.
E. Shahriari, A. Kramberger, A. Gams, A. Ude, and S. Haddadin, “Adapting to contacts: Energy tanks and task energy for passivity-based dynamic movement primitives,” in Proc. IEEE-RAS 17th Int. Conf. Humanoid Robot., 2017, pp. 136–142.
C. Liu and Z. Li, “Force tracking smooth adaptive admittance control in unknown environment,” Robotica, vol. 41, pp. 1991–2011, 2023.
J. Cacace, R. Caccavale, A. Finzi, and V. Lippiello, “Variable admittance control based on virtual fixtures for human-robot co-manipulation,” in Proc. IEEE Int. Conf. Syst., Man Cybern., 2019, pp. 1569–1574.
A. Madani, P. P. Niaz, B. Guler, Y. Aydin, and C. Basdogan, “Robot-assisted drilling on curved surfaces with haptic guidance under adaptive admittance control,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2022, pp. 3723–3730.
B. Guler, P. P. Niaz, A. Madani, Y. Aydin, and C. Basdogan, “An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning,” Mechatronics, vol. 86, 2022, Art. no. 102851.
M. Mashayekhi and M. M. Moghaddam, “EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot,” J. Biomech., vol. 138, 2022, Art. no. 111104.
P. D. Labrecque and C. Gosselin, “Variable admittance for pHRI: From intuitive unilateral interaction to optimal bilateral force amplification,” Robot. Comput.-Integr. Manuf., vol. 52, pp. 1–8, 2018.

Related Article on Qiita

CAN FD and CAN XL on arXiv
https://qiita.com/kaizen_nagoya/items/d8efb0da53cd3456f735

CAN FD & CAN XL on arXiv references
https://qiita.com/kaizen_nagoya/items/7df86c66084372a96f1d

CAN FD & CAN XL on arXiv references name order
https://qiita.com/kaizen_nagoya/items/ec5e4e4491228db534c0

CAN FD & CAN XL on arXiv references name order and related link
https://qiita.com/kaizen_nagoya/items/e6c75c799db62d078bd0

Comparison of Classical CAN, CAN FD, and CAN XL and CAN XL
https://qiita.com/kaizen_nagoya/items/773835b2bf3cddd17dd8

Comparison of CAN XL & 10BASE-T1S Functionality on Layer 1 and Layer 2
https://qiita.com/kaizen_nagoya/items/13235743ba8d7193a53b