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A Region-Based Strategy for Collaborative Roadmap Construction

Part of the Springer Tracts in Advanced Robotics book series (STAR,volume 107)

Abstract

Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning , whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success in many scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conversely, human intuition can often determine an approximate solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding low-level tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space while maintaining the probabilistic completeness property of the PRM planner. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness. We demonstrate that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.

Keywords

  • Construction Guidelines
  • Region Steering
  • Probabilistic Roadmap (PRM)
  • Avoid Region
  • Standard Template Adaptive Parallel Library (STAPL)

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Singh, A.P., Latombe, J.C., Brutlag, D.L.: A motion planning approach to flexible ligand binding. In: International Conference on Intelligent Systems for Molecular Biology (ISMB), pp. 252–261 (1999)

    Google Scholar 

  2. Lien, J.M., Pratt, E.: Interactive planning for shepherd motion. In: The AAAI Spring Symposium, March 2009

    Google Scholar 

  3. Bayazit, O.B., Song, G., Amato, N.M.: Enhancing randomized motion planners: exploring with haptic hints. In: Proceedings of IEEE International Conference on Robotics Automation (ICRA), pp. 529–536 (2000)

    Google Scholar 

  4. Reif, J.H.: Complexity of the mover’s problem and generalizations. In: Proceedings of IEEE Symposium Foundations of Computer Science (FOCS), San Juan, Puerto Rico, October 1979, pp. 421–427

    Google Scholar 

  5. Kavraki, L.E., Švestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)

    CrossRef  Google Scholar 

  6. LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)

    CrossRef  Google Scholar 

  7. Hsu, D., Latombe, J.C., Kurniawati, H.: On the probabilistic foundations of probabilistic roadmap planning. Int. J. Robot. Res. 25, 627–643 (2006)

    CrossRef  Google Scholar 

  8. Hwang, Y., Cho, K., Lee, S., Park, S., Kang, S.: Human computer cooperation in interactive motion planning. In: Proceedings of IEEE International Conference on Advanced Robotics (ICAR), pp. 571–576 (1997)

    Google Scholar 

  9. Ivanisevic, I., Lumelsky, V.J.: Configuration space as a means for augmenting human performance in teleoperation tasks. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 30(3), 471–484 (2000)

    CrossRef  Google Scholar 

  10. Lee, S., Sukhatme, G., Kim, G.J., Park, C.M.: Haptic teleoperation of a mobile robot: a user study. Presence: Teleoper. Virtual Environ. 14(3), 345–365 (2005)

    CrossRef  Google Scholar 

  11. Guo, C., Tarn, T., Xi, N., Bejczy, A.: Fusion of human and machine intelligence for telerobotic systems. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 3110–3115 (1995)

    Google Scholar 

  12. Taïx, M., Flavigné, D., Ferré, E.: Human interaction with motion planning algorithm. J. Intell. Robot. Syst. 67(3–4), 285–306 (2012)

    CrossRef  Google Scholar 

  13. Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22(10), 560–570 (1979)

    CrossRef  Google Scholar 

  14. Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C., Vallejo, D.: OBPRM: an obstacle-based PRM for 3d workspaces. In: Proceedings of the Third Workshop on the Algorithmic Foundations of Robotics (WAFR’98), pp. 155–168. A. K. Peters, Ltd., Natick (1998)

    Google Scholar 

  15. Boor, V., Overmars, M.H., van der Stappen, A.F.: The Gaussian sampling strategy for probabilistic roadmap planners. Proc. IEEE Int. Conf. Robot. Autom. (ICRA) 2, 1018–1023 (1999)

    Google Scholar 

  16. Hsu, D., Jiang, T., Reif, J., Sun, Z.: Bridge test for sampling narrow passages with probabilistic roadmap planners. In: Proceedings of IEEE International Conference on Robotics Automation (ICRA), pp. 4420–4426 (2003)

    Google Scholar 

  17. Denny, J., Amato, N.M.: Toggle PRM: a coordinated mapping of C-free and C-obstacle in arbitrary dimension. In: Algorithmic Foundations of Robotics X. (WAFR’12) of Springer Tracts in Advanced Robotics, vol. 86, pp. 297–312. Springer, Berlin/Heidelberg (2013)

    Google Scholar 

  18. Morales, M., Tapia, L., Pearce, R., Rodriguez, S., Amato, N.M.: A machine learning approach for feature-sensitive motion planning. In: Algorithmic Foundations of Robotics VI. (WAFR’04) Springer Tracts in Advanced Robotics, pp. 361–376. Springer, Berlin/Heidelberg (2005)

    Google Scholar 

  19. Berg, J., Overmars, M.: Using workspace information as a guid to non-uniform sampling in probabilistic roadmap planners. Int. J. Robot. Res. 24(12), 1055–1072 (2005)

    CrossRef  Google Scholar 

  20. Ivanisevic, I., Lumelsky, V.: Human augmentation in teleoperation of arm manipulators in an environment with obstacles. In: Proceedings IEEE International Conference on Robotics and Automation (ICRA), pp. 1994–1999 (2000)

    Google Scholar 

  21. Ivanisevic, I., Lumelsky, V.: Augmenting human performance in motion planning tasks- the configuration space approach. In: Proceedings on IEEE International Conference on Robotics and Automation (ICRA), pp. 2649–2654 (2001)

    Google Scholar 

  22. Yan, Y., Poirson, E., Bennis, F.: Integrating user to minimize assembly path planning time in plm. In: Product Lifecycle Management for Society. IFIP Advances in Information and Communication Technology, vol. 409, pp. 471–480. Springer, Berlin Heidelberg (2013)

    Google Scholar 

  23. Hokayem, P.F., Spong, M.W.: Bilateral teleoperation: an historical survey. Automatica 42, 2035–2057 (2006)

    CrossRef  MATH  MathSciNet  Google Scholar 

  24. Masone, C., Franchi, A., Bulthoff, H.H., Giordano, P.R.: Interactive planning of persistent trajectories for human-assisted navigation of mobile robots. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2641–2648 (2012)

    Google Scholar 

  25. Buss, A., Harshvardhan, Papadopoulos, I., Pearce, O., Smith, T., Tanase, G., Thomas, N., Xu, X., Bianco, M., Amato, N.M., Rauchwerger, L.: STAPL: Standard template adaptive parallel library, pp. 1–10, ACM, New York, NY, USA (2010)

    Google Scholar 

  26. Amato, N.M.: Motion planning benchmarks http://parasol.tamu.edu/groups/amatogroup/benchmarks/

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Acknowledgments

This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). J. Denny supported in part by an NSF Graduate Research Fellowship.

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Denny, J., Sandström, R., Julian, N., Amato, N.M. (2015). A Region-Based Strategy for Collaborative Roadmap Construction. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-16595-0_8

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