Cylindrical Terrain Classification Using a Compliant Robot Foot with a Flexible Tactile-Array Sensor for Legged Robots

  • Pongsiri BorijindakulEmail author
  • Noparit Jinuntuya
  • Alin Drimus
  • Poramate Manoonpong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10994)


In this paper, we present a new approach that uses a combination of a compliant robot foot with a flexible tactile-array sensor to classify different types of cylindrical terrains. The foot and sensor were installed on a robot leg. Due to their compliance and flexibility, they can passively adapt their shape to the terrains and simultaneously provide pressure feedback during walking. We applied two different methods, which are average and maximum value methods, to classify the terrains based on the feedback information. To test the approach, We performed two experimental conditions which are (1) different diameters and different materials and (2) different materials with the same cylindrical diameter. In total, we use here eleven cylindrical terrains with different diameters and materials (i.e., a 8.2-cm diameter PVC cylinder, a 7.5-cm diameter PVC cylinder, a 5.5-cm diameter PVC cylinder, a 4.4-cm diameter PVC cylinder, a 7.5-cm diameter hard paper cylinder, a 7.4-cm diameter hard paper cylinder, a 5.5-cm diameter hard paper cylinder, a 20-cm diameter sponge cylinder, a 15-cm diameter sponge cylinder, a 7.5-cm diameter sponge cylinder, and a 5.5-cm diameter sponge cylinder). The experimental results show that we can successfully classify all terrains for the maximum value method. This approach can be applied to allow a legged robot to not only walk on cylindrical terrains but also recognize the terrain feature. It thereby extends the operational range the robot towards cylinder/pipeline inspection.


Compliant robot foot Flexible tactile-array sensor Cylindrical terrains 



This work was supported by the Capacity Building on Academic Competency of KU. Students from Kasetsart University, Thailand, Centre for BioRobotics (CBR) at University of Southern Denmark (SDU, Denmark), the Thousand Talents program of China, and the Human Frontier Science Program under grant agreement no. RGP0002/2017.


  1. 1.
    Di Canio, G., Stoyanov, S., Larsen, J.C., Hallam, J., Kovalev, A., Kleinteich, T., Gorb, S.N., Manoonpong, P.: A robot leg with compliant tarsus and its neural control for efficient and adaptive locomotion on complex terrains. Artif. Life Robot. 21(3), 274–281 (2016)CrossRefGoogle Scholar
  2. 2.
    Drimus, A., Kootstra, G., Bilberg, A., Kragic, D.: Design of a flexible tactile sensor for classification of rigid and deformable objects. Robotics and Autonomous Systems 62, 3–15 (2014)CrossRefGoogle Scholar
  3. 3.
    Mrva, J., Faigl, J.: Feature extraction for terrain classification with crawling robots. Robotics In: Yaghob, J. (ed.) ITAT 2015, 2010 Current and Future, pp. 179–185. Charles University in Prague, Prague (2015)Google Scholar
  4. 4.
    Walas, K.: Terrain classification and negotiation with a walking robot. Intell. Robot. Syst. 78(3–4), 401–423 (2015). Scholar
  5. 5.
    Degrave, J., Van Cauwenbergh, R., wyffels, F., Waegeman, T., Schrauwen, B.: Terrain classification for a quadruped robot. In: Machine Learning and Applications (ICMLA). IEEE, Miami (2013).
  6. 6.
    Bermudez, F.L.C., Julian, C., Haldane, W., Abbeel, P., Fearing, R. S.: Performance analysis and terrain classification for a legged robot over rough terrain. In: Intelligent Robots and Systems, IEEE/RSJ, Vilamoura (2012).
  7. 7.
    Belter, D., Skrzypczyński, P.: Rough terrain mapping and classification for footholdselection in a walking robot. Field. Robot. 28(4), 497–528 (2011). Scholar
  8. 8.
    Kisung, K., Kwangjin K., Wansoo, K., Seungnam, Y., Changsoo H.: Performance Comparison between neural network and SVM for terrain classification of legged robot. In: SICE Annual Conference 2010, pp. 1343–1348. IEEE, Taipei (2010)Google Scholar
  9. 9.
    Poppinga, J., Birk, A., Pathak., K.: Hough based terrain classification for realtime detection of drivable ground. Field. Robot. 25(1), 67–88 (2008). Scholar
  10. 10.
    Wu, X.A., Huh, T.M., Mukherjee, R., Cutkosky, M.: Integrated ground reaction force sensing and terrain classification for small legged robots. IEEE Robot. Autom. Lett. 1(2), 1125–1132 (2016)CrossRefGoogle Scholar
  11. 11.
    Walas, K.: Tactile sensing for ground classification. J. Autom. Mobile Robot. Intell. Syst. 7(2), 18–23 (2013)Google Scholar
  12. 12.
    Gong, D., He, R., Yu, J., Zuo., G.: A pneumatic tactile sensor for co-operative robots. Sensors 17(11), 2592–2606 (2017)CrossRefGoogle Scholar
  13. 13.
    Jamali, N., Sammut, C.: Material classification by tactile sensing using surface textures. In: Robotics and Automation, pp. 2336–2341. IEEE, Anchorage (2010).
  14. 14.
    Nakamoto, H., Kobayashi, F., Kojima, F.: Shape classification using tactile information in rotation manipulation by universal robot hand. In: Abdellatif , H. (Ed.) Robotics 2010 Current and Future Challenges. ISBN: 978-953-7619-78-7Google Scholar
  15. 15.
    Drimus, A., Mátéfi-Tempfli, S.: Tactile shoe inlays for high speed pressure monitoring. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds.) ICIRA 2015. LNCS (LNAI), vol. 9245, pp. 74–81. Springer, Cham (2015). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Pongsiri Borijindakul
    • 1
    • 2
    • 3
    Email author
  • Noparit Jinuntuya
    • 1
  • Alin Drimus
    • 4
  • Poramate Manoonpong
    • 2
    • 3
  1. 1.Kasetsart UniversityBangkokThailand
  2. 2.Embodied AI & Neurorobotics Lab, Centre for Biorobotics, The Mærsk Mc-Kinney Møller InstituteUniversity of Southern DenmarkOdenseDenmark
  3. 3.Institute of Bio-inspired Structure and Surface EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  4. 4.The Mads Clausen Institute, University of Southern DenmarkSønderborgDenmark

Personalised recommendations