Skip to main content
Springer Nature Link
Log in

ImPACT-TRC Thin Serpentine Robot Platform for Urban Search and Rescue

  • Chapter
  • First Online:
Disaster Robotics

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 128))

  • 1219 Accesses

Abstract

The Active Scope Camera has self-propelled mobility with a ciliary vibration drive mechanism for inspection tasks in narrow spaces but still lacks necessary mobility and sensing capabilities for search and rescue activities. The ImPACT-TRC program aims to improve the mobility of ASC drastically by applying a new air-jet actuation system to float ASC in the air and integrate multiple sensing systems, such as vision, auditory and tactile sensing functions, to enhance the searching ability. This paper reports an overview of the air-floating-type Active Scope Camera integrated with multiple sensory functions as a thin serpentine robot platform.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Albl, C., Sugimoto, A., Pajdla, T.: Degeneracies in rolling shutter SfM. In: Proceedings of European Conference on Computer Vision, pp. 36–51 (2016)

    Chapter  Google Scholar 

  2. Ambe, Y., Yamamoto, T., Kojima, S., Takane, E., Tadakuma, K., Konyo, M., Tadokoro, S.: Use of active scope camera in the Kumamoto Earthquake to investigate collapsed houses. In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 21–27. IEEE (2016). https://doi.org/10.1109/SSRR.2016.7784272, http://ieeexplore.ieee.org/document/7784272/

  3. Ando, H., Ambe, Y., Ishii, A., Konyo, M., Tadakuma, K., Maruyama, S., Tadokoro, S.: Aerial hose type robot by water jet for fire fighting. IEEE Robot. Autom. Lett. 3(2), 1128–1135 (2018). https://doi.org/10.1109/LRA.2018.2792701

    Article  Google Scholar 

  4. Babacan, S.D., Luessi, M., Molina, R., Katsaggelos, A.K.: Sparse Bayesian methods for low-rank matrix estimation. IEEE Trans. Signal Process. 60(8), 3964–3977 (2012)

    Article  MathSciNet  Google Scholar 

  5. Bando, Y., Itoyama, K., Konyo, M., Tadokoro, S., Nakadai, K., Yoshii, K., Kawahara, T., Okuno, H.G.: Speech enhancement based on Bayesian low-rank and sparse decomposition of multichannel magnitude spectrograms. IEEE/ACM Trans. Audio Speech Lang. Process. 26(2), 215–230 (2018). https://doi.org/10.1109/TASLP.2017.2772340

    Article  Google Scholar 

  6. Bando, Y., Itoyama, K., Konyo, M., Tadokoro, S., Nakadai, K., Yoshii, K., Okuno, H.G.: Human-voice enhancement based on online RPCA for a hose-shaped rescue robot with a microphone array. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6 (2015)

    Google Scholar 

  7. Bando, Y., Itoyama, K., Konyo, M., Tadokoro, S., Nakadai, K., Yoshii, K., Okuno, H.G.: Microphone-accelerometer based 3D posture estimation for a hose-shaped rescue robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5580–5586 (2015)

    Google Scholar 

  8. Bando, Y., Itoyama, K., Konyo, M., Tadokoro, S., Nakadai, K., Yoshii, K., Okuno, H.G.: Variational Bayesian multi-channel robust NMF for human-voice enhancement with a deformable and partially-occluded microphone array. In: Proceedings of the 21st European Signal Processing Conference (EUSIPCO), pp. 1018–1022 (2016)

    Google Scholar 

  9. Bando, Y., Saruwatari, H., Ono, N., Makino, S., Itoyama, K., Kitamura, D., Ishimura, M., Takakusaki, M., Mae, N., Yamaoka, K., et al.: Low latency and high quality two-stage human-voice-enhancement system for a hose-shaped rescue robot. J. Robot. Mechatron. 29(1), 198–212 (2017)

    Article  Google Scholar 

  10. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Berlin (2007)

    Google Scholar 

  11. Bloomfield, A., Badle, N.I.: Collision awareness using vibrotactile arrays. In: IEEE Virtual Reality Conference 2007. VR’07, pp. 163–170 (2007)

    Google Scholar 

  12. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 11 (2011)

    Article  MathSciNet  Google Scholar 

  13. Chen, J.Y., Haas, E.C., Barnes, M.J.: Human performance issues and user interface design for teleoperated robots. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(6), 1231–1245 (2007)

    Article  Google Scholar 

  14. De Barros, P.G., Lindeman, R.W., Ward, M.O.: Enhancing robot teleoperator situation awareness and performance using vibro-tactile and graphical feedback. In: 2011 IEEE Symposium on 3D User Interfaces (3DUI), pp. 47–54. IEEE (2011)

    Google Scholar 

  15. Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2018)

    Article  Google Scholar 

  16. Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: Large-scale direct monocular slam. In: Proceedings of European Conference on Computer Vision, pp. 834–849. Springer (2014)

    Google Scholar 

  17. Feng, J., Xu, H., Yan, S.: Online robust PCA via stochastic optimization. In: Advances in Neural Information Processing Systems (NIPS), pp. 404–412 (2013)

    Google Scholar 

  18. Févotte, C., Dobigeon, N.: Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization. IEEE Trans. Image Process. 24(12), 4810–4819 (2015)

    Article  MathSciNet  Google Scholar 

  19. Fukuda, J., Konyo, M., Takeuchi, E., Tadokoro, S.: Remote vertical exploration by Active Scope Camera into collapsed buildings. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1882–1888. IEEE (2014). https://doi.org/10.1109/IROS.2014.6942810, http://ieeexplore.ieee.org/document/6942810/

  20. Fukuda, J., Konyo, M., Takeuchi, E., Tadokoro, S.: Remote vertical exploration by Active Scope Camera into collapsed buildings. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1882–1888 (2014). https://doi.org/10.1109/IROS.2014.6942810

  21. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN: 0521540518

    Google Scholar 

  22. Hatazaki, K., Konyo, M., Isaki, K., Tadokoro, S., Takemura, F.: Active scope camera for urban search and rescue. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2596–2602 (2007). https://doi.org/10.1109/IROS.2007.4399386, http://ieeexplore.ieee.org/document/4399386/

  23. Heyden, A., Åkström, K.: Minimal conditions on intrinsic parameters for euclidean reconstruction. In: Proceedings of Asian Conference on Computer Vision, pp. 169–176 (1998)

    Chapter  Google Scholar 

  24. Heyden, A., Åström, K.: Euclidean reconstruction from image sequences with varying and unknown focal length and principal point. In: Proceedings of Computer Vision and Pattern Recognition, pp. 438–446 (1997)

    Google Scholar 

  25. Heyden, A., Astrom, K.: Flexible calibration: Minimal cases for auto-calibration. In: Proceedings of International Conference on Computer Vision, pp. 350–355 (1999)

    Google Scholar 

  26. Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)

    Article  Google Scholar 

  27. Hioka, Y., Kingan, M., Schmid, G., Stol, K.A.: Speech enhancement using a microphone array mounted on an unmanned aerial vehicle. In: International Workshop on Acoustic Signal Enhancement (IWAENC), pp. 1–5 (2016)

    Google Scholar 

  28. Hoffman, M.D.: Poisson-uniform nonnegative matrix factorization. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5361–5364 (2012)

    Google Scholar 

  29. Ishii, A., Ambe, Y., Yamauchi, Y., Ando, H., Konyo, M., Tadakuma, K., Tadokoro, S.: Design and development of biaxial active nozzle with flexible flow channel for air floating active scope camera. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, (Accepted) (2018)

    Google Scholar 

  30. Ishikura, M., Takeuchi, E., Konyo, M., Tadokoro, S.: Shape estimation of flexible cable. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2539–2546 (2012)

    Google Scholar 

  31. Israr, A., Poupyrev, I.: Tactile brush: drawing on skin with a tactile grid display. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2019–2028. ACM (2011)

    Google Scholar 

  32. Ito, E., Okatani, T.: Self-calibration-based approach to critical motion sequences of rolling-shutter structure from motion. In: Proceedings of Computer Vision and Pattern Recognition, pp. 4512–4520 (2017)

    Google Scholar 

  33. Julier, S.J.: The scaled unscented transformation. In: American Control Conference, vol. 6, pp. 4555–4559 (2002)

    Google Scholar 

  34. Kahl, F., Triggs, B., Åström, K.: Critical motions for auto-calibration when some intrinsic parameters can vary. J. Math. Imaging Vis. 13, 131–146 (2000)

    Article  MathSciNet  Google Scholar 

  35. Kamio, S., Ambe, Y., Ando, H., Konyo, M., Tadakuma, K., Maruyama, S., Tadokoro, S.: Air-floating-type active scope camera with a flexible passive parallel mechanism for climbing rubble. In: 2016 SICE Domestic Conference on System Integration (in Japanese), pp. 0639 – 0642 (2016)

    Google Scholar 

  36. Kim, T.: Real-time independent vector analysis for convolutive blind source separation. IEEE Trans. Circuits Syst. I 57(7), 1431–1438 (2010)

    Article  MathSciNet  Google Scholar 

  37. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007)

    Google Scholar 

  38. Knapp, C., Carter, G.C.: The generalized correlation method for estimation of time delay. IEEE Trans. Acoust. Speech Signal Process. (TASSP) 24(4), 320–327 (1976)

    Article  Google Scholar 

  39. Konyo, M., Isaki, K., Hatazaki, K., Tadokoro, S., Takemura, F.: Ciliary vibration drive mechanism for active scope cameras. J. Robot. Mechatron. 20(3), 490–499 (2008). https://doi.org/10.20965/jrm.2008.p0490

    Article  Google Scholar 

  40. Lee, J., Ukawa, G., Doho, S., Lin, Z., Ishii, H., Zecca, M., Takanishi, A.: Non visual sensor based shape perception method for gait control of flexible colonoscopy robot. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 577–582 (2011)

    Google Scholar 

  41. Li, Y., et al.: Speech enhancement based on robust NMF solved by alternating direction method of multipliers. In: IEEE International Workshop on Multimedia Signal Processing (MMSP), pp. 1–5 (2015)

    Google Scholar 

  42. Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vis. 8(2), 123–151 (1992)

    Article  Google Scholar 

  43. Mazumdar A, A.H.: Pulse width modulation of water jet propulsion systems using high-speed coanda-effect valves. ASME. J. Dyn. Sys. Meas. Control. 135(5), 051019 (2013). https://doi.org/10.1115/1.4024365

    Article  Google Scholar 

  44. McMahan, W., Gewirtz, J., Standish, D., Martin, P., Kunkel, J.A., Lilavois, M., Wedmid, A., Lee, D.I., Kuchenbecker, K.J.: Tool contact acceleration feedback for telerobotic surgery. IEEE Trans. Haptics 4(3), 210–220 (2011)

    Article  Google Scholar 

  45. Miura, H., Yoshida, T., Nakamura, K., Nakadai, K.: SLAM-based online calibration for asynchronous microphone array. Adv. Robot. 26(17), 1941–1965 (2012)

    Article  Google Scholar 

  46. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  47. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)

    Article  Google Scholar 

  48. Nakadai, K., Takahashi, T., Okuno, H.G., Nakajima, H., Hasegawa, Y., Tsujino, H.: Design and implementation of robot audition system HARK – open source software for listening to three simultaneous speakers. Adv. Robot. 24(5–6), 739–761 (2011)

    Google Scholar 

  49. Namari, H., Wakana, K., Ishikura, M., Konyo, M., Tadokoro, S.: Tube-type active scope camera with high mobility and practical functionality. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3679–3686 (2012). https://doi.org/10.1109/IROS.2012.6386172, http://ieeexplore.ieee.org/document/6386172/

  50. Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: Proceedings of International Conference on Computer Vision, pp. 2320–2327. IEEE (2011)

    Google Scholar 

  51. Nugraha, A.A., Liutkus, A., Vincent, E.: Multichannel audio source separation with deep neural networks. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 24(9), 1652–1664 (2016)

    Article  Google Scholar 

  52. Okamoto, S., Konyo, M., Saga, S., Tadokoro, S.: Detectability and perceptual consequences of delayed feedback in a vibrotactile texture display. IEEE Trans. Haptics 2(2), 73–84 (2009)

    Article  Google Scholar 

  53. Ono, N., Kohno, H., Ito, N., Sagayama, S.: Blind alignment of asynchronously recorded signals for distributed microphone array. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 161–164 (2009)

    Google Scholar 

  54. Ooka, T., Fujita, K.: Virtual object manipulation system with substitutive display of tangential force and slip by control of vibrotactile phantom sensation. In: 2010 IEEE Haptics Symposium, pp. 215–218 (2010)

    Google Scholar 

  55. Pollefeys, M., Koch, R., Gool, V.L.: Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. Int. J. Comput. Vis. 31(1), 7–25 (1999)

    Article  Google Scholar 

  56. Sibert, J., Cooper, J., Covington, C., Stefanovski, A., Thompson, D., Lindeman, R.W.: Vibrotactile feedback for enhanced control of urban search and rescue robots. In: Proceedings of the IEEE International Workshop on Safety, Security and Rescue Robotics (2006)

    Google Scholar 

  57. Silva Rico, J.A., Endo, G., Hirose, S., Yamada, H.: Development of an actuation system based on water jet propulsion for a slim long-reach robot. ROBOMECH J. 4(1), 8 (2017). https://doi.org/10.1186/s40648-017-0076-4

  58. Strecha, C., von Hansen, W., Gool, V.L., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: Proceedings of Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  59. Sturm, P.: Critical motion sequences for monocular self-calibration and uncalibrated euclidean reconstruction. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1100–1105 (1997)

    Google Scholar 

  60. Suzuki, Y., Asano, F., Kim, H.Y., Sone, T.: An optimum computer-generated pulse signal suitable for the measurement of very long impulse responses. J. Acoust. Soc. Am. 97, 1119 (1995)

    Article  Google Scholar 

  61. Thomas, R.D.L.V.S., et al.: Response Times: Their Role in Inferring Elementary Mental Organization: Their Role in Inferring Elementary Mental Organization. Oxford University Press, USA (1986)

    Google Scholar 

  62. Tully, S., Kantor, G., Choset, H.: Inequality constrained Kalman filtering for the localization and registration of a surgical robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5147–5152 (2011)

    Google Scholar 

  63. Wan, E.A., et al.: The unscented Kalman filter for nonlinear estimation. In: The IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium, pp. 153–158 (2000)

    Google Scholar 

  64. Wu, C.: Visual SFM. http://ccwu.me/vsfm/

  65. Wu, C.: Towards linear-time incremental structure from motion. In: Proceedings of International Conference on 3D Vision, pp. 127–134 (2013)

    Google Scholar 

  66. Xu, Y., Hunter, I.W., Hollerbach, J.M., Bennett, D.J.: An airjet actuator system for identification of the human arm joint mechanical properties. IEEE Trans. Biomed. Eng. 38(11), 1111–1122 (1991). https://doi.org/10.1109/10.99075

    Article  Google Scholar 

  67. Yamauchi, Y., Fujimoto, T., Ishii, A., Araki, S., Ambe, Y., Konyo, M., Tadakuma, K., Tadokoro, S.: A robotic thruster that can handle hairy flexible cable of serpentine robots for disaster inspection. In: 2018 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (2018). https://doi.org/10.1109/AIM.2018.8452708

  68. Zhang, C., Florêncio, D., Zhang, Z.: Why does PHAT work well in lownoise, reverberative environments? In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2565–2568 (2008)

    Google Scholar 

  69. Zhang, L., Chen, Z., Zheng, M., He, X.: Robust non-negative matrix factorization. Front. Electr. Electron. Eng. China 6(2), 192–200 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Impulsing Paradigm Change through Disruptive Technologies (ImPACT) Tough Robotics Challenge program of Japan Science and Technology (JST) Agency.

Author information

Authors and Affiliations

  1. Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, Sendai-shi, Miyagi, 980-8579, Japan

    Masashi Konyo, Yuichi Ambe, Hikaru Nagano, Yu Yamauchi, Satoshi Tadokoro, Takayuki Okatani, Kanta Shimizu & Eisuke Ito

  2. National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7, Aomi, Koto-ku, Tokyo, 135-0064, Japan

    Yoshiaki Bando

  3. Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan

    Katsutoshi Itoyama

  4. Waseda University, 3F, 2-4-12 Okubo, Shinjuku-ku, Tokyo, 169-0072, Japan

    Hiroshi G. Okuno

Authors
  1. Masashi Konyo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuichi Ambe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hikaru Nagano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Yamauchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Satoshi Tadokoro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yoshiaki Bando
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Katsutoshi Itoyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hiroshi G. Okuno
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Takayuki Okatani
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kanta Shimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Eisuke Ito
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masashi Konyo .

Editor information

Editors and Affiliations

  1. Graduate School of Information Sciences, Tohoku University, Sendai, Japan

    Satoshi Tadokoro

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Konyo, M. et al. (2019). ImPACT-TRC Thin Serpentine Robot Platform for Urban Search and Rescue. In: Tadokoro, S. (eds) Disaster Robotics. Springer Tracts in Advanced Robotics, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-05321-5_2

Download citation

Publish with us

Policies and ethics

Search

Navigation