1 research outputs found

    Combining haptics and inertial motion capture to enhance remote control of a dual-arm robot

    Full text link
    [EN] High dexterity is required in tasks in which there is contact between objects, such as surface conditioning (wiping, polishing, scuffing, sanding, etc.), specially when the location of the objects involved is unknown or highly inaccurate because they are moving, like a car body in automotive industry lines. These applications require the human adaptability and the robot accuracy. However, sharing the same workspace is not possible in most cases due to safety issues. Hence, a multi-modal teleoperation system combining haptics and an inertial motion capture system is introduced in this work. The human operator gets the sense of touch thanks to haptic feedback, whereas using the motion capture device allows more naturalistic movements. Visual feedback assistance is also introduced to enhance immersion. A Baxter dual-arm robot is used to offer more flexibility and manoeuvrability, allowing to perform two independent operations simultaneously. Several tests have been carried out to assess the proposed system. As it is shown by the experimental results, the task duration is reduced and the overall performance improves thanks to the proposed teleoperation method.This research was funded by Generalitat Valenciana (Grants GV/2021/074 and GV/2021/181) and by the SpanishGovernment (Grants PID2020-118071GB-I00 and PID2020-117421RBC21 funded by MCIN/AEI/10.13039/501100011033). This work was also supported byCoordenacao de Aperfeiaoamento de Pessoal de Nivel Superior (CAPES Brasil) under Finance Code 001, by CEFET-MG, and by a Royal Academy of Engineering Chair in Emerging Technologies to YD.Girbés-Juan, V.; Schettino, V.; Gracia Calandin, LI.; Solanes, JE.; Demiris, Y.; Tornero, J. (2022). Combining haptics and inertial motion capture to enhance remote control of a dual-arm robot. Journal on Multimodal User Interfaces. 16(2):219-238. https://doi.org/10.1007/s12193-021-00386-8219238162Hägele M, Nilsson K, Pires JN, Bischoff R (2016) Industrial robotics. Springer, Cham, pp 1385–1422. https://doi.org/10.1007/978-3-319-32552-1_54Hokayem PF, Spong MW (2006) Bilateral teleoperation: an historical survey. Automatica 42(12):2035–2057. https://doi.org/10.1016/j.automatica.2006.06.027Son HI (2019) The contribution of force feedback to human performance in the teleoperation of multiple unmanned aerial vehicles. J Multimodal User Interfaces 13(4):335–342Jones B, Maiero J, Mogharrab A, Aguliar IA, Adhikari A, Riecke BE, Kruijff E, Neustaedter C, Lindeman RW (2020) Feetback: augmenting robotic telepresence with haptic feedback on the feet. In: Proceedings of the 2020 international conference on multimodal interaction, pp 194–203Merrad W, Héloir A, Kolski C, Krüger A (2021) Rfid-based tangible and touch tabletop for dual reality in crisis management context. J Multimodal User Interfaces. https://doi.org/10.1007/s12193-021-00370-2Schettino V, Demiris Y (2019) Inference of user-intention in remote robot wheelchair assistance using multimodal interfaces. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 4600–4606Casper J, Murphy RR (2003) Human–robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Trans Syst Man Cybern Part B (Cybern) 33(3):367–385. https://doi.org/10.1109/TSMCB.2003.811794Chen JY (2010) UAV-guided navigation for ground robot tele-operation in a military reconnaissance environment. Ergonomics 53(8):940–950. https://doi.org/10.1080/00140139.2010.500404 (pMID: 20658388.)Aleotti J, Micconi G, Caselli S, Benassi G, Zambelli N, Bettelli M, Calestani D, Zappettini A (2019) Haptic teleoperation of UAV equipped with gamma-ray spectrometer for detection and identification of radio-active materials in industrial plants. In: Tolio T, Copani G, Terkaj W (eds) Factories of the future: the Italian flagship initiative. Springer, Cham, pp 197–214. https://doi.org/10.1007/978-3-319-94358-9_9Santos Carreras L (2012) Increasing haptic fidelity and ergonomics in teleoperated surgery. PhD Thesis, EPFL, Lausanne, pp 1–188. https://doi.org/10.5075/epfl-thesis-5412Hatzfeld C, Neupert C, Matich S, Braun M, Bilz J, Johannink J, Miller J, Pott PP, Schlaak HF, Kupnik M, Werthschützky R, Kirschniak A (2017) A teleoperated platform for transanal single-port surgery: ergonomics and workspace aspects. In: IEEE world haptics conference (WHC), pp 1–6. https://doi.org/10.1109/WHC.2017.7989847Burns JO, Mellinkoff B, Spydell M, Fong T, Kring DA, Pratt WD, Cichan T, Edwards CM (2019) Science on the lunar surface facilitated by low latency telerobotics from a lunar orbital platform-gateway. Acta Astronaut 154:195–203. https://doi.org/10.1016/j.actaastro.2018.04.031Sivčev S, Coleman J, Omerdić E, Dooly G, Toal D (2018) Underwater manipulators: a review. Ocean Eng 163:431–450. https://doi.org/10.1016/j.oceaneng.2018.06.018Abich J, Barber DJ (2017) The impact of human–robot multimodal communication on mental workload, usability preference, and expectations of robot behavior. J Multimodal User Interfaces 11(2):211–225. https://doi.org/10.1007/s12193-016-0237-4Hong A, Lee DG, Bülthoff HH, Son HI (2017) Multimodal feedback for teleoperation of multiple mobile robots in an outdoor environment. J Multimodal User Interfaces 11(1):67–80. https://doi.org/10.1007/s12193-016-0230-yKatyal KD, Brown CY, Hechtman SA, Para MP, McGee TG, Wolfe KC, Murphy RJ, Kutzer MDM, Tunstel EW, McLoughlin MP, Johannes MS (2014) Approaches to robotic teleoperation in a disaster scenario: from supervised autonomy to direct control. In: IEEE/RSJ international conference on intelligent robots and systems, pp 1874–1881. https://doi.org/10.1109/IROS.2014.6942809Niemeyer G, Preusche C, Stramigioli S, Lee D (2016) Telerobotics. Springer, Cham, pp 1085–1108. https://doi.org/10.1007/978-3-319-32552-1_43Li J, Li Z, Hauser K (2017) A study of bidirectionally telepresent tele-action during robot-mediated handover. In: Proceedings—IEEE international conference on robotics and automation, pp 2890–2896. https://doi.org/10.1109/ICRA.2017.7989335Peng XB, Kanazawa A, Malik J, Abbeel P, Levine S (2018) Sfv: reinforcement learning of physical skills from videos. ACM Trans. Graph. 37(6):178:1-178:14. https://doi.org/10.1145/3272127.3275014Coleca F, State A, Klement S, Barth E, Martinetz T (2015) Self-organizing maps for hand and full body tracking. Neurocomputing 147: 174–184. Advances in self-organizing maps subtitle of the special issue: selected papers from the workshop on self-organizing maps 2012 (WSOM 2012). https://doi.org/10.1016/j.neucom.2013.10.041Von Marcard T, Rosenhahn B, Black MJ, Pons-Moll G (2017) Sparse inertial poser: automatic 3d human pose estimation from sparse Imus. In: Computer graphics forum, vol 36. Wiley, pp 349–360Zhao J (2018) A review of wearable IMU (inertial-measurement-unit)-based pose estimation and drift reduction technologies. J Phys Conf Ser 1087:042003. https://doi.org/10.1088/1742-6596/1087/4/042003Malleson C, Gilbert A, Trumble M, Collomosse J, Hilton A, Volino M (2018) Real-time full-body motion capture from video and IMUs. In: Proceedings—2017 international conference on 3D vision, 3DV 2017 (September), pp 449–457. https://doi.org/10.1109/3DV.2017.00058Du G, Zhang P, Mai J, Li Z (2012) Markerless kinect-based hand tracking for robot teleoperation. Int J Adv Robot Syst 9(2):36. https://doi.org/10.5772/50093Çoban M, Gelen G (2018) Wireless teleoperation of an industrial robot by using myo arm band. In: International conference on artificial intelligence and data processing (IDAP), pp 1–6. https://doi.org/10.1109/IDAP.2018.8620789Lipton JI, Fay AJ, Rus D (2018) Baxter’s homunculus: virtual reality spaces for teleoperation in manufacturing. IEEE Robot Autom Lett 3(1):179–186. https://doi.org/10.1109/LRA.2017.2737046Zhang T, McCarthy Z, Jow O, Lee D, Chen X, Goldberg K, Abbeel P (2018) Deep imitation learning for complex manipulation tasks from virtual reality teleoperation. In: IEEE international conference on robotics and automation (ICRA), pp 5628–5635. https://doi.org/10.1109/ICRA.2018.8461249Hannaford B, Okamura AM (2016) Haptics. Springer, Cham, pp 1063–1084. https://doi.org/10.1007/978-3-319-32552-1_42Rodríguez J-L, Velàzquez R (2012) Haptic rendering of virtual shapes with the Novint Falcon. Proc Technol 3:132–138. https://doi.org/10.1016/J.PROTCY.2012.03.014Teklemariam HG, Das AK (2017) A case study of phantom omni force feedback device for virtual product design. Int J Interact Des Manuf (IJIDeM) 11(4):881–892. https://doi.org/10.1007/s12008-015-0274-3Karbasizadeh N, Zarei M, Aflakian A, Masouleh MT, Kalhor A (2018) Experimental dynamic identification and model feed-forward control of Novint Falcon haptic device. Mechatronics 51:19–30. https://doi.org/10.1016/j.mechatronics.2018.02.013Georgiou T, Demiris Y (2017) Adaptive user modelling in car racing games using behavioural and physiological data. User Model User-Adapted Interact 27(2):267–311. https://doi.org/10.1007/s11257-017-9192-3Son HI (2019) The contribution of force feedback to human performance in the teleoperation of multiple unmanned aerial vehicles. J Multimodal User Interfaces 13(4):335–342. https://doi.org/10.1007/s12193-019-00292-0Ramírez-Fernández C, Morán AL, García-Canseco E (2015) Haptic feedback in motor hand virtual therapy increases precision and generates less mental workload. In: 2015 9th international conference on pervasive computing technologies for healthcare (PervasiveHealth), pp 280–286. https://doi.org/10.4108/icst.pervasivehealth.2015.260242Saito Y, Raksincharoensak P (2019) Effect of risk-predictive haptic guidance in one-pedal driving mode. Cognit Technol Work 21(4):671–684. https://doi.org/10.1007/s10111-019-00558-3Girbés V, Armesto L, Dols J, Tornero J (2016) Haptic feedback to assist bus drivers for pedestrian safety at low speed. IEEE Trans Haptics 9(3):345–357. https://doi.org/10.1109/TOH.2016.2531686Girbés V, Armesto L, Dols J, Tornero J (2017) An active safety system for low-speed bus braking assistance. IEEE Trans Intell Transp Syst 18(2):377–387. https://doi.org/10.1109/TITS.2016.2573921Escobar-Castillejos D, Noguez J, Neri L, Magana A, Benes B (2016) A review of simulators with haptic devices for medical training. J Med Syst 40(4):104. https://doi.org/10.1007/s10916-016-0459-8Coles TR, Meglan D, John NW (2011) The role of haptics in medical training simulators: a survey of the state of the art. IEEE Trans Haptics 4(1):51–66. https://doi.org/10.1109/TOH.2010.19Okamura AM, Verner LN, Reiley CE, Mahvash M (2010) Haptics for robot-assisted minimally invasive surgery. In: Kaneko M, Nakamura Y (eds) Robotics research. Springer tracts in advanced robotics, vol 66. Springer, Berlin, pp 361–372. https://doi.org/10.1007/978-3-642-14743-2_30Ehrampoosh S, Dave M, Kia MA, Rablau C, Zadeh MH (2013) Providing haptic feedback in robot-assisted minimally invasive surgery: a direct optical force-sensing solution for haptic rendering of deformable bodies. Comput Aided Surg 18(5–6):129–141. https://doi.org/10.3109/10929088.2013.839744Ju Z, Yang C, Li Z, Cheng L, Ma H (2014) Teleoperation of humanoid Baxter robot using haptic feedback. In: 2014 international conference on multisensor fusion and information integration for intelligent systems (MFI). IEEE, pp 1–6. https://doi.org/10.1109/MFI.2014.6997721Clark JP, Lentini G, Barontini F, Catalano MG, Bianchi M, O’Malley MK (2019) On the role of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation. In: International conference on robotics and automation (ICRA), pp 5187–5193. https://doi.org/10.1109/ICRA.2019.8793652Gracia L, Solanes JE, Muñoz-Benavent P, Miro JV, Perez-Vidal C, Tornero J (2018) Adaptive sliding mode control for robotic surface treatment using force feedback. Mechatronics 52:102–118. https://doi.org/10.1016/j.mechatronics.2018.04.008Zhu D, Xu X, Yang Z, Zhuang K, Yan S, Ding H (2018) Analysis and assessment of robotic belt grinding mechanisms by force modeling and force control experiments. Tribol Int 120:93–98. https://doi.org/10.1016/j.triboint.2017.12.043Smith C, Karayiannidis Y, Nalpantidis L, Gratal X, Qi P, Dimarogonas DV, Kragic D (2012) Dual arm manipulation—a survey. Robot Auton Syst 60(10):1340–1353. https://doi.org/10.1016/j.robot.2012.07.005Girbés-Juan V, Schettino V, Demiris Y, Tornero J (2021) Haptic and visual feedback assistance for dual-arm robot teleoperation in surface conditioning tasks. IEEE Trans Haptics 14(1):44–56. https://doi.org/10.1109/TOH.2020.3004388Tunstel EW Jr, Wolfe KC, Kutzer MD, Johannes MS, Brown CY, Katyal KD, Para MP, Zeher MJ (2013) Recent enhancements to mobile bimanual robotic teleoperation with insight toward improving operator control. Johns Hopkins APL Tech Digest 32(3):584García A, Solanes JE, Gracia L, Muñoz-Benavent P, Girbés-Juan V, Tornero J (2021) Bimanual robot control for surface treatment tasks. Int J Syst Sci. https://doi.org/10.1080/00207721.2021.1938279Jasim IF, Plapper PW, Voos H (2014) Position identification in force-guided robotic peg-in-hole assembly tasks. Proc CIRP 23((C)):217–222. https://doi.org/10.1016/j.procir.2014.10.077Song HC, Kim YL, Song JB (2016) Guidance algorithm for complex-shape peg-in-hole strategy based on geometrical information and force control. Adv Robot 30(8):552–563. https://doi.org/10.1080/01691864.2015.1130172Kramberger A, Gams A, Nemec B, Chrysostomou D, Madsen O, Ude A (2017) Generalization of orientation trajectories and force-torque profiles for robotic assembly. Robot Auton Syst 98:333–346. https://doi.org/10.1016/j.robot.2017.09.019Pliego-Jiménez J, Arteaga-Pérez MA (2015) Adaptive position/force control for robot manipulators in contact with a rigid surface with unknown parameters. In: European control conference (ECC), pp 3603–3608. https://doi.org/10.1109/ECC.2015.7331090Gierlak P, Szuster M (2017) Adaptive position/force control for robot manipulator in contact with a flexible environment. Robot Auton Syst 95:80–101. https://doi.org/10.1016/j.robot.2017.05.015Solanes JE, Gracia L, Muñoz-Benavent P, Miro JV, Girbés V, Tornero J (2018) Human–robot cooperation for robust surface treatment using non-conventional sliding mode control. ISA Trans 80:528–541. https://doi.org/10.1016/j.isatra.2018.05.013Ravandi AK, Khanmirza E, Daneshjou K (2018) Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control. Appl Soft Comput 70:864–874. https://doi.org/10.1016/j.asoc.2018.05.048Solanes JE, Gracia L, Muñoz-Benavent P, Esparza A, Miro JV, Tornero J (2018) Adaptive robust control and admittance control for contact-driven robotic surface conditioning. Robot Comput Integr Manuf 54:115–132. https://doi.org/10.1016/j.rcim.2018.05.003Perez-Vidal C, Gracia L, Sanchez-Caballero S, Solanes JE, Saccon A, Tornero J (2019) Design of a polishing tool for collaborative robotics using minimum viable product approach. Int J Comput Integr Manuf 32(9):848–857. https://doi.org/10.1080/0951192X.2019.1637026Chen F, Zhao H, Li D, Chen L, Tan C, Ding H (2019) Contact force control and vibration suppression in robotic polishing with a smart end effector. Robot Comput Integr Manuf 57:391–403. https://doi.org/10.1016/j.rcim.2018.12.019Mohammad AEK, Hong J, Wang D, Guan Y (2019) Synergistic integrated design of an electrochemical mechanical polishing end-effector for robotic polishing applications. Robot Comput Integr Manuf 55:65–75. https://doi.org/10.1016/j.rcim.2018.07.005Waldron KJ, Schmiedeler J (2016) Kinematics. Springer, Cham, pp 11–36. https://doi.org/10.1007/978-3-319-32552-1_2Featherstone R, Orin DE (2016) Dynamics. Springer, Cham, pp 37–66. https://doi.org/10.1007/978-3-319-32552-1_3Wen K, Necsulescu D, Sasiadek J (2008) Haptic force control based on impedance/admittance control aided by visual feedback. Multimed Tools Appl 37(1):39–52. https://doi.org/10.1007/s11042-007-0172-1Tzafestas C, Velanas S, Fakiridis G (2008) Adaptive impedance control in haptic teleoperation to improve transparency under time-delay. In: IEEE international conference on robotics and automation, pp 212–219. https://doi.org/10.1109/ROBOT.2008.4543211Chiaverini S, Oriolo G, Maciejewski AA (2016) Redundant robots. Springer, Cham, pp 221–242. https://doi.org/10.1007/978-3-319-32552-1_10Ogata K (1987) Discrete-time control systems. McGraw-Hill, New YorkGarcía A, Girbés-Juan V, Solanes JE, Gracia L, Perez-Vidal C, Tornero J (2020) Human–robot cooperation for surface repair combining automatic and manual modes. IEEE Access 8:154024–154035. https://doi.org/10.1109/ACCESS.2020.301450
    corecore