Hostname: page-component-669899f699-b58lm Total loading time: 0 Render date: 2025-04-30T23:57:04.576Z Has data issue: false hasContentIssue false
  • English
  • Français

Kinematic analysis, workspace definition, and self-collision avoidance of a quasi-spherical parallel manipulator

Published online by Cambridge University Press:  23 September 2024

Daniel Pacheco Quiñones Open the ORCID record for Daniel Pacheco Quiñones [Opens in a new window] ,
Daniela Maffiodo Open the ORCID record for Daniela Maffiodo [Opens in a new window]  and
Amine Laribi Open the ORCID record for Amine Laribi [Opens in a new window]
Daniel Pacheco Quiñones*
Affiliation:
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
Daniela Maffiodo
Affiliation:
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
Amine Laribi
Affiliation:
Department of GMSC, Pprime Institute, University of Poitiers, CNRS, ISEA-ENSMA, UPR 3346, Poitiers, France
*
Corresponding author: Daniel Pacheco Quiñones; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Bilateral teleoperation has witnessed significant development since the mid-20th century, addressing challenges related to human presence in environments with constraints or a lack of skilled professionals. This article presents the kinematic and self-collision analyses of the quasi-spherical parallel manipulator, a three-legged parallel robot used as a haptic master device. The device is designed for remote center of motion-constrained operation in the telesurgical field. Inverse and forward kinematics are thoroughly analyzed to study working modes, singular configurations, and implement a haptic control architecture. The research explores the operative and reachable workspaces of the possible working modes, comparing them to find the most suitable one. Results highlight how the addition of the self-collision phenomenon impacts the working mode choice, drastically reducing most of the modes’ operative workspaces. An anti-collision control algorithm is finally introduced to maintain the architecture within its reachable workspace.

Keywords

parallel manipulatorshaptic deviceskinematic analysisworkspace analysiscontrol of robotic systemsspherical manipulatorsself-collisionsingularity analysismaster devicecollision avoidance
Type
Research Article
Information
Robotica , First View , pp. 1 - 26
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Hokayem, P. F. and Spong, M. W., “Bilateral teleoperation: An historical survey,” Automatica 42(12), 2035–2057 (2006). doi: 10.1016/j.automatica.2006.06.027.CrossRefGoogle Scholar
Goertz, R. C., “Remote-control manipulator, ” (1953). US2632574A.Google Scholar
Ferrell, W. R., “Remote manipulation with transmission delay,” IEEE Trans Hum Fact Electr HFE-6(1), 24–32 (1965). doi: 10.1109/THFE.1965.6591253.CrossRefGoogle Scholar
Imaida, T., Yokokohji, Y., Doi, T., Oda, M. and Yoshikawa, T., “Ground-space bilateral teleoperation of ETS-VII robot arm by direct bilateral coupling under 7-s time delay condition,” IEEE Trans Robot Autom 20(3), 499–511 (2004).CrossRefGoogle Scholar
Jakuba, M. V., German, C. R., Bowen, A. D., Whitcomb, L. L., Hand, K., Branch, A., Chien, S. and McFarland, C., “Teleoperation and Robotics Under Ice: Implications for Planetary Exploration,” In: 2018 IEEE Aerospace Conference, (2018) pp. 1–14.Google Scholar
Evans, C. R., Medina, M. G. and Dwyer, A. M., “Telemedicine and telerobotics: from science fiction to reality,” Updates Surg 70(3), 357–362 (2018).CrossRefGoogle ScholarPubMed
Choi, P. J., Oskouian, R. J. and Tubbs, R. S., “Telesurgery: past, present, and future,” Cureus 10(5), e2716 (2018). doi: 10.7759/cureus.2716.Google ScholarPubMed
Barba, P., Stramiello, J., Funk, E. K., Richter, F., Yip, M. C. and Orosco, R. K., “Remote telesurgery in humans: a systematic review,” Surg Endosc 36(5), 2771–2777 (2022). doi: 10.1007/s00464-022-09074-4.CrossRefGoogle ScholarPubMed
Chu, M., Cui, Z., Zhang, A., Yao, J., Tang, C., Fu, Z., Nathan, A. and Gao, S., “Multisensory fusion, haptic, and visual feedback teleoperation system under ioT framework,” IEEE Internet Things J 9(20), 19717–19727 (2022).CrossRefGoogle Scholar
Bolopion, A. and Régnier, S., “A review of haptic feedback teleoperation systems for Micromanipulation and microassembly,” IEEE Trans Autom Sci Eng 10(3), 496–502 (2013). doi: 10.1109/TASE.2013.2245122.CrossRefGoogle Scholar
Bayraktaroglu, Z. Y., Argin, O. F. and Haliyo, S., “A modular bilateral haptic control framework for teleoperation of robots,” Robotica 37(2), 338–357 (2019). doi: 10.1017/S0263574718001042.CrossRefGoogle Scholar
Tian, W., Fan, M., Zeng, C., Liu, Y., He, D. and Zhang, Q., “Telerobotic spinal surgery based on 5G network: the First 12 Cases,” Neurospine 17(1), 114–120 (2020). doi: 10.14245/ns.1938454.227.CrossRefGoogle Scholar
Lopez, I. B., Benzakour, A., Mavrogenis, A., Benzakour, T., Ahmad, A. and Lemée, J.-M., “Robotics in spine surgery: systematic review of literature,” Int Orthop 47(2), 447–456 (2023). doi: 10.1007/s00264-022-05508-9.CrossRefGoogle ScholarPubMed
Rovetta, A., Sala, R., Wen, X., Cosmi, F., Togno, A. and Milanesi, S., “Telerobotic surgery project for laparoscopy,” Robotica 13(4), 397–400 (1995).CrossRefGoogle Scholar
Meskini, M., Saafi, H., Mlika, A., Arsicault, M., Zeghloul, S. and Laribi, M. A., “Development of a novel hybrid haptic (nHH) device with a remote center of rotation dedicated to laparoscopic surgery,” Robotica 41(10), 3175–3194 (2023).CrossRefGoogle Scholar
Satcher, R. L., Bogler, O., Hyle, L., Lee, A., Simmons, A., Williams, R., Hawk, E., Matin, S. and Brewster, A. M., “Telemedicine and telesurgery in cancer care: inaugural conference at MD anderson cancer center,” J Surg Oncol 110(4), 353–359 (2014). doi: 10.1002/jso.23652.CrossRefGoogle ScholarPubMed
Chaker, A., Mlika, A., Laribi, M. A., Romdhane, L. and Zeghloul, S., “Synthesis of spherical parallel manipulator for dexterous medical task,” Frontiers of Mechanical Engineering 7(2), 150–162 (2012).CrossRefGoogle Scholar
Saafi, H., Laribi, M. A. and Zeghloul, S., “Redundantly actuated 3-RRR spherical parallel manipulator used as a haptic device: improving dexterity and eliminating singularity,” Robotica 33(5), 1113–1130 (2015). doi: 10.1017/S0263574714001751.CrossRefGoogle Scholar
Saafi, H., Laribi, M. A., Zeghloul, S. and Arsicault, M., “On the development of a new master device used for medical tasks,” J Mech Robot 10(4), 044501 (2018). doi: 10.1115/1.4039590.CrossRefGoogle Scholar
Saafi, H., Laribi, M. A. and Zeghloul, S., “Forward kinematic model resolution of a special spherical parallel manipulator: Comparison and real-time validation,” Robotics 9(3), 62 (2020). doi: 10.3390/robotics9030062.CrossRefGoogle Scholar
Bonev, I. A., Chablat, D. and Wenger, P., “Working and Assembly Modes of the Agile Eye,” In: IEEE International Conference Robotics and Automation 2006, (2006) pp. 2317–2322.Google Scholar
Enferadi, J. and Shahi, A., “A closed-form solution for the position analysis of a novel fully spherical parallel manipulator,” Robotica 33(10), 2114–2136 (2015).CrossRefGoogle Scholar
Zarkandi, S., “Kinematic analysis and optimal design of a novel 3-PRR spherical parallel manipulator,” Proc IMechE Pt C J Mech Eng Sci 235(4), 693–712 (2020).CrossRefGoogle Scholar
Sadeghian, H., Zokaei, F. and Jazi, S. H., “Constrained kinematic control in minimally invasive robotic surgery subject to remote center of motion constraint,” J Intell Robot Syst 95(3-4), 901–913 (2019). doi: 10.1007/s10846-018-0927-0.CrossRefGoogle Scholar
Trabelsi, A., Sandoval, J., Mlika, A., Lahouar, S., Zeghloul, S., Cau, J. and Laribi, M. A., “Optimal Multi-robot Placement Based on Capability Map for Medical Applications,” In: Advances in Service and Industrial Robotics, (2022) pp. 333–342. RAAD 2022. Mech. Mach. Sci.CrossRefGoogle Scholar
Quiroz-Omana, J. J. and Adorno, B. V., “Whole-body control with (Self) collision avoidance using vector field inequalities,” IEEE Robot Autom Lett 4(4), 4048–4053 (2019).CrossRefGoogle Scholar
Fang, H., Chen, J. and Dou, L., “Dynamic self-collision detection and prevention for 2-DOF robot arms using interval-based analysis,” J Mech Sci Technol 25(8), 2077–2087 (2011).CrossRefGoogle Scholar
Yang, X., Wang, H., Zhang, C. and Chen, K., “A method for mapping the boundaries of collision-free reachable workspaces,” Mech Mach Theory 45(7), 1024–1033 (2010). doi: 10.1016/j.mechmachtheory.2010.02.002.CrossRefGoogle Scholar
Bai, S., Hansen, M. R. and Angeles, J., “A robust forward-displacement analysis of spherical parallel robots,” Mech Mach Theory 44(12), 2204–2216 (2009). doi: 10.1016/j.mechmachtheory.2009.07.005.CrossRefGoogle Scholar