Wireless UWB Sensor System for Robot Gripper Monitoring in Non-cooperative Environments

  • Pavol GalajdaEmail author
  • Maria Svecova
  • Milos Drutarovsky
  • Stanislav Slovak
  • Martin Pecovsky
  • Miroslav Sokol
  • Dusan Kocur
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 14)


In this chapter, the ultra-wideband (UWB) technology for localization and tracking estimation of the robot gripper (behind obstacles) in industrial environments is presented. We investigate the possibilities of the UWB radar sensor network (UWB-SN) employing the centralized data fusion method that can significantly improve tracking capabilities in a complex industrial environment. The UWB-SN hardware nodes that use a new wireless UWB sensor with an embedded controller to detect and track online or off-line movement of robot gripper is also presented. This hardware node uses M-sequence UWB radar front-end, and low-cost ARM based quad-core microcomputer (ARM-MC) as a main signal processing block. The ARM-MC based on Raspberry Pi provides processing power for the pre-processing of received raw radar signals, algorithms for detection and estimation of target’s coordinates, and finally compression of data sent to the central node (CN). Low-rate data streams (3600–6000 bits/s/node) of compressed target coordinates are sent from each sensor node (SN) to the CN by using RF transceivers with integrated ARM microcontroller. The chapter contains experimental results from measurements where SNs and antennas are located behind the wall or opaque material. Experimental testing confirmed the power of real-time performance of developed UWB-SN hardware and acceptable precision of software. The introduced modular architecture of UWB-SN can be used for fast development and testing of new real-time localization and tracking applications required for visual feedback to robots. It enables their safe orientation in the industrial environment, coping with obstacles and at the same time cooperate as well as avoid contact with humans.



The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to thank the Slovak Cultural and Educational Grant Agency (KEGA) under the Contract No. 062TUKE-4/2017, the Slovak Research and Development Agency under the contract No. APVV-15-0692 and Scientific Grant Agency (VEGA) under the contract No. 1/0772/17.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pavol Galajda
    • 1
    Email author
  • Maria Svecova
    • 2
  • Milos Drutarovsky
    • 1
  • Stanislav Slovak
    • 1
  • Martin Pecovsky
    • 1
  • Miroslav Sokol
    • 1
  • Dusan Kocur
    • 1
  1. 1.Faculty of Electrical Engineering and Informatics, Department of Electronics and Multimedia CommunicationsTechnical University of KosiceKosiceSlovakia
  2. 2.Faculty of Electrical Engineering and Informatics, Department of Mathematics and Theoretical InformaticsTechnical University of KosiceKosiceSlovakia

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