Abstract
Unlike the conventional haptic detection with the tactile sensor or Force Sensing Resistor (FSR) sensor, this paper proposes a new algorithm for tactile sensing unit that air pressure sensors are implemented to represent the tactile degree of the robot hand which can play more accurate haptic feedback. Meanwhile, in order to optimize the performance of the tactile sensing unit, several target objects are trained with the help of Artificial Neural Network (ANN) that gives the linear output values according to the constant mass when the robot hand holds different target objects. In addition, Arrival of Time (A.o.T) algorithm is utilized for recognizing the touch points of the robot hand when the target object is compressed by the tactile sensing device. The optimal output positions can be selected through amounts of tests with various grasp positions in the haptic sensing part for the reason that different pressure-points distribution facilitates the optimization mapping. Experiments show that the proposed method can be applied for Human Robot Interaction (HRI) effectively and efficiently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cramphorn, L., Ward-Cherrier, B., Lepora, N.F.: Tactile manipulation with biomimetic active touch. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2016)
Nathan, L., Alex, C., Conrad, K., Raia, H., John, L.: From pixels to percepts: highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor. IEEE Robot. Autom. Lett. 4(2), 2101–2107 (2019)
Schmitz, A., et al.: Tactile object recognition using deep learning and dropout. In: 2014 IEEE-RAS International Conference on Humanoid Robots. IEEE (2014)
Baishya, S.S., Bäuml, B.: Robust material classification with a tactile skin using deep learning. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2016)
Claudio, C., Vikram, R.: A wearable low-cost device based upon force-sensing resistors to detect single-finger forces. In: IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) (2014)
Bahadır, S.K.: Identification and modeling of sensing capability of force sensing resistor integrated to E-textile structure. IEEE Sens. J. 18(23), 9770–9780 (2018)
Dang, H., Allen, P.K.: Stable grasping under pose uncertainty using tactile feedback. Auton. Robot. 36(4), 309–330 (2014)
Vinicius, F., Thiago, E.O., Katerina, E., Emil, M.P.: Stable grasping and object reorientation with a three-fingered robotic hand. In: IEEE International Symposium on Robotics and Intelligent Sensors (IRIS2017) (2017)
Wan, Q., Adams, R.P., Howe, R.D.: Variability and predictability in tactile sensing during grasping. In: IEEE International Conference on Robotics and Automation (ICRA) (2016)
Kim, D.E., Kim, K.S., Park, J.H., Ailing, L., Lee, J.M.: Stable grasping of objects using air pressure sensors on a robot hand. In: International Conference on Control, Automation and Systems (ICCAS 2018) (2018)
Chang, C.-H.: Deep and shallow architecture of multilayer neural networks. IEEE Transact. Neural Netw. Learn. Syst. 26(10), 2477–2486 (2015)
Gong, M., et al.: A multiobjective sparse feature learning model for deep neural networks. IEEE Transact. Neural Netw. Learn. Syst. 26(12), 3263–3277 (2015)
Păvăloiu, I.B., et al.: Feedforward multilayer phase-based neural networks. In: 12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL). IEEE (2014)
Rika, A., Mia, K., Johannes, A.S., Danica, K.: Global search with bernoulli alternation kernel for task-oriented grasping informed by simulation. In: 2nd Conference on Robot Learning (CoRL 2018) (2018)
Acknowledgment
This research is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No.10073147.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jeong, DK., Kim, DE., Ailimg, L., Lee, JM. (2019). Artificial Neural Network Based Tactile Sensing Unit for Robotic Hand. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11740. Springer, Cham. https://doi.org/10.1007/978-3-030-27526-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-27526-6_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27525-9
Online ISBN: 978-3-030-27526-6
eBook Packages: Computer ScienceComputer Science (R0)