Intelligent Service Robotics

, Volume 12, Issue 1, pp 27–43 | Cite as

The robot skin placement problem: a new technique to place triangular modules inside polygons

  • Fulvio MastrogiovanniEmail author
  • Xuenan Guo
Original Research Paper


Providing robots with large-scale robot skin is a challenging goal, especially when considering surfaces characterised by different shapes and curvatures. The problem originates from technological advances in tactile sensing, and in particular from two requirements: (i) covering the largest possible area of a robot’s surface with tactile sensors to be able to detect accidental or purposive contacts, and (ii) doing it using cheap, replicable hardware modules to keep production and manufacturing costs low. Given modules of a specific shape, the problem of optimally placing them requires to maximise the number of modules that can be fixed on the selected robot body part. Differently from previous approaches, which are based on methods inspired by computational geometry (e.g. packing), we propose a novel layout design method inspired by physical insights, referred to as Iterative Placement (ItPla), which arranges modules as if physical forces acted on them. A number of case studies from the literature are considered to evaluate the proposed approach.


Tactile sensing Robot skin Optimal placement 



  1. 1.
    Alirezaei H, Nagakubo A, Kuniyoshi Y (2007) A highly stretchable tactile distribution sensor for smooth surfaced humanoids. In: Proceedings of the 2007 IEEE-RAS international conference on humanoid robots (HUMANOIDS 2007), Pittsburgh, PA, USAGoogle Scholar
  2. 2.
    Anghinolfi D, Cannata G, Mastrogiovanni F, Nattero C, Paolucci M (2012) Heuristic approaches for the optimal wiring in large scale robotic skin design. Comput Oper Res 39(11):2715–2724MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Anghinolfi D, Cannata G, Mastrogiovanni F, Nattero C, Paolucci M (2013) On the problem of the automated design of large-scale robot skin. IEEE Trans Autom Sci Eng 10(4):1087–1100CrossRefzbMATHGoogle Scholar
  4. 4.
    Anghinolfi D, Cannata G, Mastrogiovanni F, Nattero C, Paolucci M (2014) Application and experimental validation of pheromone design in ant colony optimisation: the problem of robot skin wiring. Appl Artif Intell 28(3):292–321CrossRefzbMATHGoogle Scholar
  5. 5.
    Argall BD, Billard A (2010) A survey of tactile human–robot interactions. Robot Auton Syst 58(10):1159–1176CrossRefGoogle Scholar
  6. 6.
    Baglini E, Youssefi S, Mastrogiovanni F, Cannata G (2014) A real-time distributed architecture for large-scale tactile sensing. In: Proceedings of the 2014 IEEE/RSJ international conference on intelligent robots and systems (IROS 2014), Chicago, ILGoogle Scholar
  7. 7.
    Beetz M, Bartels G, Albu-Schäffer A, Bálint-Benczédi F, Belder R, Beßler D, Haddadin S, Maldonado A, Mansfeld N, Wiedemeyer T, Weitschat R, Worch J-H (2015) Robotic agents capable of natural and safe physical interaction with human co-workers. In: Proceedings of the 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS 2015), Hamburg, GermanyGoogle Scholar
  8. 8.
    Bhattacharjee T, Jain A, Vaish S, Killpack MD, Kemp CC (2013) Tactile sensing over articulated joints with stretchable sensors. In: Proceedings of the 2013 world haptics conference (WHC 2013), Daejeon, South KoreaGoogle Scholar
  9. 9.
    Cannata G, Denei S, Mastrogiovanni F (2010) Tactile sensing: steps to artificial somatosensory maps. In: Proceedings of the 2010 IEEE international symposium on robot and human interactive communication (RO-MAN 2010), Viareggio, ItalyGoogle Scholar
  10. 10.
    Dahiya RS, Metta G, Valle M, Sandini G (2010) Tactile sensing—from humans to humanoids. IEEE Trans Robot 26(1):1–20CrossRefGoogle Scholar
  11. 11.
    Dahiya RS, Valle M (2013) Robotic tactile sensing. Springer, BerlinCrossRefGoogle Scholar
  12. 12.
    Darvish K, Wanderlingh F, Bruno B, Simetti E, Mastrogiovanni F, Casalino G (2018) Flexible human–robot cooperation models for assisted shop-floor tasks. Mechatronics 51:97–114CrossRefGoogle Scholar
  13. 13.
    Dautenhahn K (2007) Socially intelligent robots: dimensions of human–robot interaction. Philos Trans R Soc B Biol Sci 362(1480):679–704CrossRefGoogle Scholar
  14. 14.
    Denei S, Mastrogiovanni F, Cannata G (2015) Towards the creation of tactile maps for robots and their use in robot contact motion control. Robot Auton Syst 63:293–308CrossRefGoogle Scholar
  15. 15.
    Eades P (1984) A heuristic for graph drawing. Congr Numerantium 42:149–160MathSciNetGoogle Scholar
  16. 16.
    Goodrich MA, Schultz AC (2007) Human-robot interaction: a survey. Found Trends Hum Comput Interact 1(3):203–275CrossRefzbMATHGoogle Scholar
  17. 17.
    Kaboli N, Long A, Cheng G (2015) Humanoids learn touch modalities identification via multi-modal robotic skin and robust tactile descriptors. Adv Robot 29(21):1411–1425CrossRefGoogle Scholar
  18. 18.
    Kramer R, Majidi C, Wood RJ (2011) Wearable tactile keypad with stretchable artificial skin. In: Proceedings of the 2011 IEEE international conference on robotics and automation (ICRA 2011), Shanghai, ChinaGoogle Scholar
  19. 19.
    Loi A, Basiricó L, Cosseddu P, Lai S, Barbaro M, Bonfiglio A, Maiolino P, Baglini E, Denei S, Mastrogiovanni F, Cannata G (2013) Organic bendable and stretchable field effect devices for sensing applications. IEEE Sens J 13(12):4764–4772CrossRefGoogle Scholar
  20. 20.
    Maiolino P, Maggiali M, Cannata G, Metta G, Natale L (2013) A flexible and robust large scale capacitive tactile system for robots. IEEE Sens J 13(10):3910–3917CrossRefGoogle Scholar
  21. 21.
    Maiolino P, Galantini F, Mastrogiovanni F, Gallone G, Cannata G, Carpi F (2015) Soft dielectrics for capacitive sensing in robot skins: performance of different elastomer types. Sens Actuators A Phys 226:37–47CrossRefGoogle Scholar
  22. 22.
    Matarić M (2006) Socially assistive robotics. IEEE Intell Syst 5(3–4):81–83Google Scholar
  23. 23.
    Mittendorfer P, Yoshida E, Cheng G (2015) Realising whole-body tactile interactions with a self-organising, multi-modal artificial skin on a humanoid robot. Adv Robot 29(1):51–67CrossRefGoogle Scholar
  24. 24.
    Muscari L, Seminara L, Mastrogiovanni F, Valle M, Capurro M, Cannata G (2013) Real-time reconstruction of contact shapes for large-area robot skin. In: Proceedings of the 2013 IEEE international conference on robotics and automation (ICRA 2013), Karlsruhe, GermanyGoogle Scholar
  25. 25.
    Quinn NR, Breuer MA (1979) A force directed component placement procedure for printed circuit boards. IEEE Trans Circuit Syst 26(6):377–388CrossRefzbMATHGoogle Scholar
  26. 26.
    Schmitz A, Maiolino P, Maggiali M, Natale L, Cannata G, Metta G (2011) Methods and technologies for the implementation of large-scale robot tactile sensors. IEEE Trans Robot 27(2):297–312CrossRefGoogle Scholar
  27. 27.
    Tóth GF (1972) Lagerungen in der Ebene, auf der Kugel und in Raum. Springer, BerlinCrossRefzbMATHGoogle Scholar
  28. 28.
    Youssefi S, Denei S, Mastrogiovanni F, Cannata G (2015) A real-time data acquisition and processing framework for large-scale robot skin. Robot Auton Syst 68:86–103CrossRefGoogle Scholar
  29. 29.
    Youssefi S, Denei S, Mastrogiovanni F, Cannata G (2015) Skinware 2.0: a real-time middleware for robot skin. SoftwareX 3:6–12CrossRefGoogle Scholar
  30. 30.
    Wang R, Qi X, Luo Y, Dong J (2014) A new quasi-human algorithm for solving the packing problem of unit equilateral triangles. Abstr Appl Anal 2014(308474):1–8MathSciNetGoogle Scholar
  31. 31.
    Wei X, Joneja A, Tang K (2015) An improved algorithm for the automated design of large-scale robot skin. IEEE Trans Autom Sci Eng 12(1):372–377CrossRefGoogle Scholar
  32. 32.
    Wei X, Zhao B, Joneja A, Xi J (2017) On the polygon containment problem on an isometric grid. IEEE Trans Autom Sci Eng 14(2):1075–1083CrossRefGoogle Scholar
  33. 33.
    Williams R (1979) The geometrical foundation of natural structure: a source book of design. Dover Publications Inc, New YorkGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of GenoaGenoaItaly
  2. 2.University of California, San DiegoSan DiegoUSA

Personalised recommendations