Abstract
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.
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References
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)
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)
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)
Walas, K.: Terrain classification and negotiation with a walking robot. Intell. Robot. Syst. 78(3–4), 401–423 (2015). https://doi.org/10.1007/s10846-014-0067-0
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). https://doi.org/10.1109/ICMLA.2013.39
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). https://doi.org/10.1109/IROS.2012.6386243
Belter, D., Skrzypczyński, P.: Rough terrain mapping and classification for footholdselection in a walking robot. Field. Robot. 28(4), 497–528 (2011). https://doi.org/10.1002/rob.20397
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)
Poppinga, J., Birk, A., Pathak., K.: Hough based terrain classification for realtime detection of drivable ground. Field. Robot. 25(1), 67–88 (2008). https://doi.org/10.1002/rob.20227
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)
Walas, K.: Tactile sensing for ground classification. J. Autom. Mobile Robot. Intell. Syst. 7(2), 18–23 (2013)
Gong, D., He, R., Yu, J., Zuo., G.: A pneumatic tactile sensor for co-operative robots. Sensors 17(11), 2592–2606 (2017)
Jamali, N., Sammut, C.: Material classification by tactile sensing using surface textures. In: Robotics and Automation, pp. 2336–2341. IEEE, Anchorage (2010). https://doi.org/10.1109/ROBOT.2010.5509675
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: 978-953-7619-78-7
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). https://doi.org/10.1007/978-3-319-22876-1_7
Acknowledgements
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.
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Borijindakul, P., Jinuntuya, N., Drimus, A., Manoonpong, P. (2018). Cylindrical Terrain Classification Using a Compliant Robot Foot with a Flexible Tactile-Array Sensor for Legged Robots. In: Manoonpong, P., Larsen, J., Xiong, X., Hallam, J., Triesch, J. (eds) From Animals to Animats 15. SAB 2018. Lecture Notes in Computer Science(), vol 10994. Springer, Cham. https://doi.org/10.1007/978-3-319-97628-0_12
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