Recently, people with upper arm disabilities due to neurological disorders, stroke or old age are receiving robotic assistance to perform several activities such as shaving, eating, brushing and drinking. Although the full potential of robotic assistance lies in the use of fully autonomous robotic systems, these systems are limited in design due to the complexities and the associated risks. Hence, rather than the shared controlled or active robotic systems used for such tasks around the head, an adaptive compliance control scheme-based autonomous robotic system for beard shaving assistance is proposed. The system includes an autonomous online face detection and tracking as well as selected geometrical features-based beard region estimation using the Kinect RGB-D camera. Online trajectory planning for achieving the shaving task is enabled; with the capability of online re-planning trajectories in case of unintended head pose movement and occlusion. Based on the dynamics of the UR-10 6-DOF manipulator using ADAMS and MATLAB, an adaptive force tracking impedance controller whose parameters are tuned using Genetic Algorithm (GA) with force/torque constraints is developed. This controller can regulate the contact force under head pose changing and varying shaving region stiffness by adjusting the target stiffness of the controller. Simulation results demonstrate the system capability to achieve beard shaving autonomously with varying environmental parameters that can be extended for achieving other tasks around the head such as feeding, drinking and brushing.
HallABackonjaUPainterI, et al. Acceptance and perceived usefulness of robots to assist with activities of daily living and healthcare tasks. Assist Technol2019; 31: 133–140.
2.
RobinsonHMacdonaldBBroadbentE.The role of healthcare robots for older people at home: a review. Int J Soc Robotics2014; 6: 575–591.
3.
MitznerTLChenTLKempCC, et al. Identifying the potential for robotics to assist older adults in different living environments. Int J Soc Robotics2014; 6: 213–227.
4.
WeberDJSalatinBAGrindleGG.The role of assistive robotics in the lives of persons with disability. Am J Phys Med Rehab2010; 89: 509–521.
5.
BilyeaASethNNesathuraiS, et al. Robotic assistants in personal care: a scoping review. Med Eng Phys2017; 49: 1–6.
6.
ChenTLCiocarlieMCousinsS, et al. Robots for humanity: using assistive robotics to empower people with disabilities. IEEE Rob Autom Magaz2013; 20: 30–39.
7.
HawkinsKPGricePMChenTL, et al. Assistive mobile manipulation for self-care tasks around the head. In: IEEE symposium on computational intelligence in robotic rehabilitation and assistive technologies, Orlando, FL, USA, 2014, pp.16–25. New York: IEEE.
8.
SchröerSKillmannIFrankB, et al. An autonomous robotic assistant for drinking. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 26 May 2015, pp.6482–6487. New York: IEEE.
OngKWSeetGSimSK. Sharing and trading in a human-robot system (chapter 29). In Kordic V, Lazinica A and Merdan M (eds) Cutting edge robotics, Rijeka: IntechOpen, 2005, pp. 467–483.
11.
MaheuVArchambaultPSFrappierJ, et al. Evaluation of the jaco robotic arm: clinico-economic study for powered wheelchair users with upper-extremity disabilities. In: IEEE international conference on rehabilitation robotics, Zurich, Switzerland, 2011, pp.1–5. New York: IEEE.
12.
HirzingerGBrunnerBDietrichJ, et al. Sensor-based space robotics-rotex and its telerobotic features. IEEE Trans Robot Automat1993; 9: 649–663.
13.
El-HussienyHAssalSFMAbouelsoudAA, et al. A novel intention prediction strategy for a shared control tele-manipulation system in unknown environments. In: Proceedings –2015. IEEE international conference on mechatronics, ICM, Nagoya, Japan, 2015, pp.204–209. New York: IEEE.
14.
ArrichielloFDi LilloPDi VitoD, et al. Assistive robot operated via P300-based brain computer interface. In: IEEE international conference on robotics and automation, Singapore, 2017, pp.6032–6037. New York: IEEE.
15.
TsumakiYKonTSuginumaA, et al. Development of a skincare robot. In: Proceedings – IEEE international conference on robotics and automation, Pasadena, CA, USA, 2008, pp.2963–2968. New York: IEEE.
16.
HawkinsKPKingCTlC, et al. Informing assistive robots with models of contact forces from able-bodied face wiping and shaving. In: IEEE international symposium on robot and human interactive communication, Paris, France, 2012, pp.251–258. New York: IEEE.
17.
ViolaPJonesMJ.Robust real-time face detection. Int J Computer Vision2004; 57: 137–154.
18.
LeTHNLuuKZhuC, et al. Semi self-training beard/moustache detection and segmentation simultaneously. Image Vision Comput2017; 58: 214–223.
19.
WangJGYauWY.Real-time moustache detection by combining image decolorization and texture detection with applications to facial gender recognition. Machine Vision Appl2014; 25: 1089–1099.
20.
JiaCYangTWangC, et al. A new fast filtering algorithm for a 3D point cloud based on RGB-D information. PLoS One2019; 14: e0220253–21.
21.
DhahriRBelaidS.A new method to detect and remove a beard from 3D human face model. Ijor2016; 27: 201.
22.
SchindlbeckCHaddadinS. Unified passivity-based cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks. In: IEEE international conference on robotics and automation (ICRA), Seattle, WA, USA, 2015, pp.440–447. New York: IEEE.
23.
SchiaviRGrioliGSenS, et al. Vsa-ii: a novel prototype of variable stiffness actuator for safe and performing robots interacting with humans. In: IEEE international conference on robotics and automation, Pasadena, CA, USA, 2008, pp.2171–2176. New York: IEEE.
24.
SadunASJalaniJSukorJA.An overview of active compliance control for a robotic hand. ARPN J Eng Appl Sci2016; 11: 11872–11876.
25.
AjaniOSAssalSFMEl-HussienyH. Towards development of an autonomous robotic system for beard shaving assistance for disabled people. 2019. In: IEEE international conference on systems, man and cybernetics (SMC), Bari, Italy, 6–9 October 2019, pp.3435–3440. New York: IEEE.
26.
Universal Robots. UR10 User Manual. Technical report, 2014.
TsakirakiE. Real-time head motion tracking for brain positron emission tomography using Microsoft. PhD Thesis, KTH Royal Institute of Technology, 2016.
29.
TervenJRDianaMC.Kin2. A kinect 2 toolbox for MATLAB. Sci Comput Program2016; 130: 97–106.
30.
Rahman M. Beginning Microsoft Kinect for Windows SDK 2.0. Berkeley, CA: Apress, 2017, pp.1–297
31.
KlasingKAlthoffDWollherrD, et al. Comparison of surface normal estimation methods for range sensing applications. In: IEEE international conference on robotics and automation, Kobe, Japan, 2009. 3206–3211. New York: IEEE.
32.
HuLWangYZhangJ, et al. A massage robot based on Chinese massage therapy. Ind Robot2013; 40: 158–172.
33.
HoganN.Impedance control of industrial robots. Rob Comput – Integr Manuf1984; 1: 97–113.
34.
JungSBinSYHsiaTC. Experimental studies of neural network impedance force control for robot manipulators. In: IEEE International conference on robotics and automation, Seoul, South Korea, 2001, pp. 3453–3458. New York: IEEE.
35.
SongPYuYZhangX.A tutorial survey and comparison of impedance control on robotic manipulation. Robotica2019; 37: 801–836.
36.
MehdiHBoubakerO.Stiffness and impedance control using lyapunov theory for robot-aided rehabilitation. Int J Soc Robotics2012; 4: 107–119.
37.
NaeemMAAssalSFM.Development of a 4-DOF cane robot to enhance walking activity of elderly. Proc IMechE, Part C: J Mechanical Engineering Science. Epub ahead of print 18 February 2019. DOI: 10.1177/0954406219830440.
38.
HaceAUranSJezernikK, et al. Robust sliding mode based impedance control. In: Proceedings of IEEE international conference on intelligent engineering systems, Budapest, Hungary, 1997, pp.77–82. New York: IEEE.
39.
ChoHCParkJHKimK, et al. Sliding-mode-based impedance controller for bilateral teleoperation under varying time-delay. In: Proceedings 2001 ICRA: IEEE International Conference on Robotics and Automation, Seoul, South Korea, 2001, pp. 1025–1030. New York: IEEE.
40.
SerajiHColbaughR.Force tracking in impedance control. Int J Rob Res1997; 16: 97–117.
41.
LeeKBussM.Force tracking impedance control with variable target stiffness. Ifac2008; 41: 6751–6756.
42.
AjaniOSAssalSFM.Hybrid force tracking impedance control-based autonomous robotic system for tooth brushing assistance of disabled people. IEEE Trans Med Robot Bionics2020; 2: 649–660.
