Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2018, Article ID 4358747, 14 pages
https://doi.org/10.1155/2018/4358747
Research Article

Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model

1State key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, China
2Shenzhen Academy of Aerospace Technology, Shenzhen, China
3Istituto Italiano di Tecnologia, Via Morego 30, Genova, Italy

Correspondence should be addressed to Xin Wang; moc.liamg@s70tihgnawnix and Fei Chen; ti.tii@nehc.ief

Received 11 June 2018; Revised 18 August 2018; Accepted 19 September 2018; Published 1 November 2018

Guest Editor: Andy Annamalai

Copyright © 2018 Fusheng Zha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” International Journal of Robotics Research, vol. 30, no. 7, pp. 846–894, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. L. Kavraki, P. Svestka, and M. H. Overmars, Probabilistic roadmaps for path planning in high-dimensional configuration spaces, vol. 1994, Unknown Publisher, 1994.
  3. S. M. LaValle and J. J. Kuffner Jr., “Randomized kinodynamic planning,” International Journal of Robotics Research, vol. 20, no. 5, pp. 378–400, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Janson, E. Schmerling, A. Clark, and M. Pavone, “Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions,” International Journal of Robotics Research, vol. 34, no. 7, pp. 883–921, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. G. v. Bergen, “Efficient Collision Detection of Complex Deformable Models using AABB Trees,” Journal of Graphics Tools, vol. 2, no. 4, pp. 1–13, 1997. View at Publisher · View at Google Scholar
  6. G. Sánchez and J.-C. Latombe, “On delaying collision checking in PRM planning: Application to multi-robot coordination,” International Journal of Robotics Research, vol. 21, no. 1, pp. 5–26, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Dalibard and J.-P. Laumond, “Linear dimensionality reduction in random motion planning,” International Journal of Robotics Research, vol. 30, no. 12, pp. 1461–1476, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Hsu, T. Jiang, J. Reif, and Z. Sun, “The bridge test for sampling narrow passages with probabilistic roadmap planners,” in Proceedings of the 2003 IEEE International Conference on Robotics and Automation, pp. 4420–4426, Taiwan, September 2003. View at Scopus
  9. L. Zhang and D. Manocha, “An efficient retraction-based RRT planner,” in Proceedings of the 2008 IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 3743–3750, USA, May 2008. View at Scopus
  10. M. Saha, J.-C. Latombe, Y.-C. Chang, and F. Prinz, “Finding narrow passages with probabilistic roadmaps: The small-step retraction method,” Autonomous Robots, vol. 19, no. 3, pp. 301–319, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Burns and O. Brock, “Information theoretic construction of probabilistic roadmaps,” in Proceedings of the Intelligent Robots and Systems, 2003.(IROS 2003) 2003 IEEE/RSJ International Conference, vol. 1, pp. 650–655, 2003.
  12. O. Arslan and P. Tsiotras, “Machine learning guided exploration for sampling-based motion planning algorithms,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, pp. 2646–2652, Germany, October 2015. View at Scopus
  13. O. Arslan and P. Tsiotras, The role of vertex consistency in sampling-based algorithms for optimal motion planning, 2012, arXiv preprint arXiv:1204.6453.
  14. J. Bialkowski, M. Otte, and E. Frazzoli, “Free-configuration biased sampling for motion planning,” in Proceedings of the 2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013, pp. 1272–1279, Japan, November 2013. View at Scopus
  15. B. Burns and O. Brock, Model-based motion planning, vol. 51, Computer Science Department Faculty Publication Series, 2004.
  16. C. Yang, X. Wang, L. Cheng, and H. Ma, “Neural-Learning-Based Telerobot Control with Guaranteed Performance,” IEEE Transactions on Cybernetics, vol. 47, no. 10, pp. 3148–3159, 2017. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Pan and D. Manocha, “Fast and robust motion planning with noisy data using machine learning,” in Proceedings of the 30th International Conference on Machine Learning, 2013.
  18. K. Yang, S. Keat Gan, and S. Sukkarieh, “A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV,” Advanced Robotics, vol. 27, no. 6, pp. 431–443, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Huh and D. D. Lee, “Learning high-dimensional Mixture Models for fast collision detection in Rapidly-Exploring Random Trees,” in Proceedings of the Robotics and Automation (ICRA), 2016 IEEE International Conference, pp. 63–69, 2016.
  20. B. Ichter, J. Harrison, and M. Pavone, Learning sampling distributions for robot motion planning, 2017, arXiv preprint arXiv:1709.05448.
  21. C. Yang, G. Peng, Y. Li, R. Cui, L. Cheng, and Z. Li, “Neural networks enhanced adaptive admittance control of optimized robot-environment interaction,” IEEE Transactions on Cybernetics, vol. 99, p. 12, 2018. View at Google Scholar
  22. M. Phillips, B. J. Cohen, S. Chitta, and M. Likhachev, “E-Graphs: Bootstrapping Planning with Experience Graphs,” Robotics: Science and Systems, vol. 5, no. 1, 2012. View at Google Scholar
  23. C. Yang, C. Chen, W. He, R. Cui, and Z. Li, “Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives,” in Proceedings of the EEE transactions on neural networks and learning systems, vol. 99, pp. 1–11, 2018.
  24. N. Vlassis and A. Likas, “A greedy EM algorithm for Gaussian mixture learning,” Neural Processing Letters, vol. 15, no. 1, pp. 77–87, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Q. Li and A. R. Barron, “Mixture density estimation,” Advances in neural infor- mation processing systems, pp. 279–285, 2000. View at Google Scholar
  26. J. Pan, S. Chitta, and D. Manocha, “FCL: A general purpose library for collision and proximity queries,” pp. 3859–3866. View at Scopus
  27. I. A. Şucan, M. Moll, and L. Kavraki, “The open motion planning library,” IEEE Robotics and Automation Magazine, vol. 19, no. 4, pp. 72–82, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. J. J. Kuffner Jr. and S. M. la Valle, “RRT-connect: an efficient approach to single-query path planning,” in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 995–1001, April 2000. View at Scopus
  29. D. Hsu, J.-C. Latombe, and R. Motwani, “Path planning in expansive configuration spaces,” in Proceedings of the 1997 IEEE International Conference on Robotics and Automation, ICRA. Part 3 (of 4), pp. 2719–2726, April 1997. View at Scopus
  30. I. A. Sucan and L. E. Kavraki, “Kinodynamic motion planning by interior-exterior cell exploration,” in Algorithmic Foundation of Robotics VIII, pp. 449–464, Springer, Berlin, Heidelberg, 2009. View at Google Scholar
  31. A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “OctoMap: An efficient probabilistic 3D mapping framework based on octrees,” Autonomous Robots, vol. 34, no. 3, pp. 189–206, 2013. View at Publisher · View at Google Scholar · View at Scopus