Fast Self-collision Detection Method for Walking Robots

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 440)

Abstract

The paper presents fast collision detection method for walking robots. In the paper we present the module which uses triangle mesh of the robot to detect collisions between parts of the robot. To this end, the triangle to triangle intersection test is applied. To speed up the computation the bounding box test is carried out at the beginning. We show the properties and performance of the collision detection module. Then, we propose the method which uses Gaussian mixture to determine self-collision model. The method is significantly faster than the method which uses triangle meshes but less precise. The collision detection methods can be applied during motion planning as well as during execution of the planned motion to detect infeasible configurations of the robot.

Keywords

Walking robots Self-collisions detection Gaussian mixture 

References

  1. 1.
    Belter, D., Łabecki, P., Skrzypczyński, P.: Adaptive motion planning for autonomous rough terrain traversal with a walking robot. J. Field Robot. (in print)Google Scholar
  2. 2.
    Belter, D., Walas, K.: A compact walking robot—flexible research and development platform, recent advances in automation. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Robotics and Measuring Techniques, vol. 267, pp. 343–352 (2014)Google Scholar
  3. 3.
    Belter, D., Skrzypczyński, P.: Posture optimization strategy for a statically stable robot traversing rough terrain. In: IEEE/RSJ 2012 International Conference on Intelligent Robots and Systems, pp. 2204–2209. Vilamoura, Portugal (2012)Google Scholar
  4. 4.
    Belter, D., Skrzypczyński, P.: Integrated Motion Planning For A Hexapod Robot Walking on Rough Terrain, 18th IFAC World Congress. Milan, Italy (2011)Google Scholar
  5. 5.
    Benoudjit, N., Archambeau, C., Lendasse, A., Lee, J., Verleysen, M.: Width optimization of the Gaussian kernels in radial basis function networks. In: Proceedings of European Symposium on Artificial Neural Networks, pp. 425–432. Bruges (2002)Google Scholar
  6. 6.
    Dahlquist, G., Björck, A.: Numerical Methods. Series in Automatic Computing. Prentice Hall, New Jersey (1974)Google Scholar
  7. 7.
    De Luca, A., Flacco, F.: Integrated control for pHRI: collision avoidance, detection, reaction and collaboration. In: IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 288–295 (2012)Google Scholar
  8. 8.
    Ericson, C.: Real-time Collision Detection. CRC Press (2004)Google Scholar
  9. 9.
    Geva, A.: ColDet 3D Collision Detection. http://sourceforge.net/projects/coldet/ (2015)
  10. 10.
    Haddadin, S., Albu-Schäffer, A., De Luca, A., Hirzinger, G.: Collision detection and reaction: a contribution to safe physical human-robot interaction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356–3363 (2008)Google Scholar
  11. 11.
    Hermann, A., Klemm, S., Xue, Z., Roennau, A., Dillmann, R.: GPU-based real-time collision detection for motion execution in mobile manipulation planning. In: IEEE International Conference on Advanced Robotics, pp. 1–7. Montevideo (2013)Google Scholar
  12. 12.
    Hermann, A., Bauer, J., Klemm, S., Dillmann, R.: Mobile manipulation planning optimized for GPGPU voxel collision detection in high resolution live 3D-maps. In: 41st International Symposium on Robotics, ISR/Robotik 2014, pp. 1–8. Munich, Germany (2014)Google Scholar
  13. 13.
    Hermann, A., Drews, F., Bauer, J., Klemm, S., Roennau, A., Dillmann, R.: Unified GPU voxel collision detection for mobile manipulation planning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4154–4160. Chicago (2014)Google Scholar
  14. 14.
    Hutter, M., Gehring, C., Bloesch, M., Hoepflinger, M., Remy, C.D., Siegwart, R.: StarlETH: a compliant quadrupedal robot for fast, efficient, and versatile locomotion. In: Proceedings of the International Conference on Climbing and Walking Robots (CLAWAR), pp. 483–490 (2012)Google Scholar
  15. 15.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. Piscataway (1995)Google Scholar
  16. 16.
    Klosowski, J.T., Held, M., Mitchell, J.S.B., Sowizral, H., Zikan, K.: Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE Trans. Vis. Comput. Graph. 4(1), 21–36 (1998)CrossRefGoogle Scholar
  17. 17.
    Min, T., Kim, Y.J., Manocha, D.: C2A: controlled conservative advancement for continuous collision detection of polygonal models. In: IEEE International Conference on Robotics and Automation, pp. 849–854 (2009)Google Scholar
  18. 18.
    Möller, T.: A fast triangle-triangle intersection test. J. Graph. Tools 2, 25–30 (1997)CrossRefGoogle Scholar
  19. 19.
    Pan, J., Manocha, D.: GPU-based parallel collision detection for fast motion planning. Int. J. Robot. Res. 31(2), 187–200 (2012)CrossRefGoogle Scholar
  20. 20.
    Plagemann, C., Fox, D., Burgard, W.: Efficient failure detection on mobile robots using particle filters with Gaussian process proposals. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2185–2190 (2007)Google Scholar
  21. 21.
    Schwarzer, F., Saha, M., Latombe, J.C.: Exact Collision Checking of Robot Paths. Springer (2004)Google Scholar
  22. 22.
    Shen, H., Heng, P.A., Tang, Z.: A fast triangle-triangle overlap test using signed distances. J. Graph. Tools 8(1), 3–15 (2003)Google Scholar
  23. 23.
    Tropp, O., Tal, A., Shimshoni, I.: A fast triangle to triangle intersection test for collision detection. Comput. Animation Virtual Worlds 17(5), 527–535 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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