Abstract
This paper is the early report of available market and scientific solutions allowing intuitive control. Manipulator control is presented in form of the Human Machine Interaction loop that describes both machine possibilities of sensing the human control and human possibilities of sensing the machine state. The survey is presented in form of the description and discussion of the advantages, disadvantages and usability of the available solutions. The aim of the research is to chose the proper path of development of the new way of intuitive control.
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References
Adikari, S., McDonald, C.: User and usability modeling for HCI/HMI: a research design. In: 2006 International Conference on Information and Automation, pp. 151–154 (2006). https://doi.org/10.1109/ICINFA.2006.374099
Blachuta, M., Grygiel, R., Czyba, R., Szafranski, G.: Attitude and heading reference system based on 3D complementary filter. In: 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 851–856 (2014). https://doi.org/10.1109/MMAR.2014.6957468
Blackler, A., Popovic, V., Mahar, D.P.: Intuitive use of products. In: Design Research Society (DSR) International Conference: Common Ground, pp. 1–15. Staffordshire University Press (2002)
Bosscher, P.M., Summer, M.D.: Telematic interface with control signal scaling based on force sensor feedback (2014). US Patent 8,918,215
Gîrbacia, F., Postelnicu, C., Voinea, G.D.: Towards using natural user interfaces for robotic arm manipulation. In: International Conference on Robotics in Alpe-Adria Danube Region, pp. 188–193. Springer, Heidelberg (2019)
Hildebrandt, M., Christensen, L., Kerdels, J., Albiez, J., Kirchner, F.: Realtime motion compensation for ROV-based tele-operated underwater manipulators. In: OCEANS 2009-EUROPE, pp. 1–6. IEEE (2009)
Hinchet, R., Vechev, V., Shea, H., Hilliges, O.: Dextres: wearable haptic feedback for grasping in VR via a thin form-factor electrostatic brake. In: The 31st Annual ACM Symposium on User Interface Software and Technology, pp. 901–912. ACM (2018)
Jhang, L.H., Santiago, C., Chiu, C.S.: Multi-sensor based glove control of an industrial mobile robot arm. In: 2017 International Automatic Control Conference (CACS), pp. 1–6. IEEE (2017)
Katyal, K.D., Brown, C.Y., Hechtman, S.A., Para, M.P., McGee, T.G., Wolfe, K.C., Murphy, R.J., Kutzer, M.D., Tunstel, E.W., McLoughlin, M.P., et al.: Approaches to robotic teleoperation in a disaster scenario: from supervised autonomy to direct control. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1874–1881. IEEE (2014)
Kim, T.W., Marani, G., Yuh, J.: Underwater vehicle manipulators, pp. 407–422. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-16649-0_17
Kofman, J., Wu, X., Luu, T.J., Verma, S.: Teleoperation of a robot manipulator using a vision-based human-robot interface. IEEE Trans. Ind. Electron. 52(5), 1206–1219 (2005)
Le Ba, N., Oh, S., Sylvester, D., Kim, T.T.H.: A 256 pixel, 21.6 \(\mu \)w infrared gesture recognition processor for smart devices. Microelectron. J. 86, 49–56 (2019)
Li, S., Rameshwar, R., Votta, A.M., Onal, C.D.: Intuitive control of a robotic arm and hand system with pneumatic haptic feedback. IEEE Rob. Autom. Lett. 4(4), 4424–4430 (2019)
Li, Z., Huang, B., Ajoudani, A., Yang, C., Su, C.Y., Bicchi, A.: Asymmetric bimanual control of dual-arm exoskeletons for human-cooperative manipulations. IEEE Trans. Rob. 34(1), 264–271 (2017)
Liang, H., Yuan, J., Thalmann, D., Zhang, Z.: Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization. Vis. Comput. 29(6–8), 837–848 (2013)
Lu, Z., Zhang, Y., Cheng, D., Wang, S., et al.: Method of dual manipulator human-friendly control based on wireless motion capture technology. In: 2018 11th International Workshop on Human Friendly Robotics (HFR), pp. 31–35. IEEE (2018)
Ma, J., Khang, G.: Quantification and adjustment of pressure and vibration elicited by transcutaneous electrical stimulation. Int. J. Precis. Eng. Manuf. 19(8), 1233–1238 (2018)
MacKenzie, I.S.: Input devices and interaction techniques for advanced computing. Virt. Environ. Adv. Interf. Des., 437–470 (1995)
Mardiyanto, R., Utomo, M.F.R., Purwanto, D., Suryoatmojo, H.: Development of hand gesture recognition sensor based on accelerometer and gyroscope for controlling arm of underwater remotely operated robot. In: 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 329–333 (2017). https://doi.org/10.1109/ISITIA.2017.8124104
Nuelle, K., Schulz, M.J., Aden, S., Dick, A., Munske, B., Gaa, J., Kotlarski, J., Ortmaier, T.: Force Sensing, Low-Cost Manipulator in Mobile Robotics. In: 3rd IEEE International Conference on Control, Automation and Robotics (ICCAR), Nagoya, Japan, 22–24 April 2017, pp. 196–201. IEEE (2017)
Premarathna, C.P., Ruhunage, I., Chathuranga, D.S., Lalitharatne, T.D.: Haptic feedback system for an artificial prosthetic hand for object grasping and slip detection: a preliminary study. In: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2304–2309. IEEE (2018)
Stańczyk, K., Poświata, A., Roksela, A., Mikulski, M.: Assessment of muscle fatigue, strength and muscle activation during exercises with the usage of robot luna EMG, among patients with multiple sclerosis. In: International Conference on Information Technologies in Biomedicine, pp. 117–128. Springer, Heidelberg (2019)
Suau, X., Alcoverro, M., López-Méndez, A., Ruiz-Hidalgo, J., Casas, J.R.: Real-time fingertip localization conditioned on hand gesture classification. Image Vis. Comput. 32(8), 522–532 (2014)
Wang, K.J., Zheng, C.Y., Mao, Z.H.: Human-centered, ergonomic wearable device with computer vision augmented intelligence for VR multimodal human-smart home object interaction. In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 767–768. IEEE (2019)
Yamakawa, Y., Matsui, Y., Ishikawa, M.: Human–robot collaborative manipulation using a high-speed robot hand and a high-speed camera. In: 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), pp. 426–429. IEEE (2018)
Zhang, H., Yan, X., Li, H.: Ergonomic posture recognition using 3D view-invariant features from single ordinary camera. Autom. Constr. 94, 1–10 (2018)
Zhang, K., Follmer, S.: Electrostatic adhesive brakes for high spatial resolution refreshable 2.5 D tactile shape displays. In: 2018 IEEE Haptics Symposium (HAPTICS), pp. 319–326. IEEE (2018)
Acknowledgments
The research is financed by Polish National Centre for Research and Development under project number POIR.01.01.01-00-0266/18: Inteligentny, efektywny system prowadzenia specjalistycznych prac podwodnych (Smart and effective system for performing specialized subsea works) realized by SR Robotics sp. z o.o.
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Grzejszczak, T., Babiarz, A., Bieda, R., Jaskot, K., Kozyra, A., Ściegienka, P. (2020). Selection of Methods for Intuitive, Haptic Control of the Underwater Vehicle’s Manipulator. In: Bartoszewicz, A., Kabziński, J., Kacprzyk, J. (eds) Advanced, Contemporary Control. Advances in Intelligent Systems and Computing, vol 1196. Springer, Cham. https://doi.org/10.1007/978-3-030-50936-1_43
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