From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human–Robot Interaction
Author:
Giovinazzo Francesco1ORCID, Grella Francesco1, Sartore Marco2, Adami Manuela2, Galletti Riccardo2, Cannata Giorgio1ORCID
Affiliation:
1. Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), Università di Genova, Via all’Opera Pia 13, 16145 Genova, Italy 2. ElbaTech Srl, Via Roma 10, 57030 Marciana, Italy
Abstract
The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a close collaboration between humans and robots. Industrial manufacturing environments must be easily adaptable to different task requirements, possibly taking into account the ergonomics and production line flexibility. Therefore, external sensing infrastructures such as cameras and motion capture systems may not be sufficient or suitable as they limit the shop floor reconfigurability and increase setup costs. In this paper, we present the technological advancements leading to the realization of ProxySKIN, a skin-like sensory system based on networks of distributed proximity sensors and tactile sensors. This technology is designed to cover large areas of the robot body and to provide a comprehensive perception of the surrounding space. ProxySKIN architecture is built on top of CySkin, a flexible artificial skin conceived to provide robots with the sense of touch, and arrays of Time-of-Flight (ToF) sensors. We provide a characterization of the arrays of proximity sensors and we motivate the design choices that lead to ProxySKIN, analyzing the effects of light interference on a ToF, due to the activity of other sensing devices. The obtained results show that a large number of proximity sensors can be embedded in our distributed sensing architecture and incorporated onto the body of a robotic platform, opening new scenarios for complex applications.
Reference39 articles.
1. Prediction of Human Activity Patterns for Human–Robot Collaborative Assembly Tasks;Zanchettin;IEEE Trans. Ind. Inform.,2019 2. Landi, C.T., Cheng, Y., Ferraguti, F., Bonfè, M., Secchi, C., and Tomizuka, M. (2019, January 3–8). Prediction of Human Arm Target for Robot Reaching Movements. Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China. 3. Bascetta, L., Ferretti, G., Rocco, P., Ardo, H., Bruyninckx, H., Demeester, E., and Di Lello, E. (2011, January 25–30). Towards Safe Human-Robot Interaction in Robotic Cells: An Approach Based on Visual Tracking and Intention Estimation. Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA. 4. Roitberg, A., Perzylo, A., Somani, N., Giuliani, M., Rickert, M., and Knoll, A. (2014, January 9–12). Human activity recognition in the context of industrial human-robot interaction. Proceedings of the Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, Siem Reap, Cambodia. 5. Tellaeche, A., Maurtua, I., and Ibarguren, A. (2015, January 8–11). Human Robot Interaction in Industrial Robotics. Examples from Research Centers to Industry. Proceedings of the 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Luxembourg.
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