Skip to main content
SpringerLink
Log in
Book cover

Algorithmic Foundations of Robotics XII pp 640–655Cite as

Dynamic Region-biased Rapidly-exploring Random Trees

  • Chapter
  • First Online:

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 13))

Abstract

Current state-of-the-art motion planners rely on samplingbased planning to explore the problem space for a solution. However, sampling valid configurations in narrow or cluttered workspaces remains a challenge. If a valid path for the robot correlates to a path in the workspace, then the planning process can employ a representation of the workspace that captures its salient topological features. Prior approaches have investigated exploiting geometric decompositions of the workspace to bias sampling; while beneficial in some environments, complex narrow passages remain challenging to navigate.

In this work, we present Dynamic Region-biased RRT, a novel samplingbased planner that guides the exploration of a Rapidly-exploring Random Tree (RRT) by moving sampling regions along an embedded graph that captures the workspace topology. These sampling regions are dynamically created, manipulated, and destroyed to greedily bias sampling through unexplored passages that lead to the goal. We show that our approach reduces online planning time compared with related methods on a set of maze-like problems.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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 thirdWorkshop on the Algorithmic Foundations of Robotics. pp. 155–168. A. K. Peters, Ltd., Natick, MA, USA (1998), (WAFR ‘98)

    Google Scholar 

  2. van den Berg, J.P., Overmars, M.H.: Using workspace information as a guide to non-uniform sampling in probabilistic roadmap planners. Int. J. Robot. Res. 24(12), 1055–1071 (2005)

    Google Scholar 

  3. Buss, A.A., Harshvardhan, Papadopoulos, I., Pearce, O., Smith, T.G., Tanase, G., Thomas, N., Xu, X., Bianco, M., Amato, N.M., Rauchwerger, L.: STAPL: standard template adaptive parallel library. In: Proceedings of of SYSTOR 2010: The 3rd Annual Haifa Experimental Systems Conference, Haifa, Israel, May 24-26, 2010. pp. 1–10. ACM, New York, NY, USA (2010), http://doi.acm.org/10.1145/1815695.1815713

  4. Canutescu, A.A., Dunbrack, Jr., R.L.: Cyclic coordinate descent: A robotics algorithm for protein loop closure. Protein Sci. 12(5), 963–972 (2003)

    Google Scholar 

  5. Cheng, P., LaValle, S.: Reducing metric sensitivity in randomized trajectory design. In: Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS). vol. 1, pp. 43–48 vol.1 (2001)

    Google Scholar 

  6. Choset, H., Burdick, J.: Sensor-based exploration: The hierarchial generalized voronoi graph. Int. J. Robot. Res. 19(2), 96–125 (2000)

    Google Scholar 

  7. Denny, J., Sandstrom, R., Julian, N., Amato, N.M.: A region-based strategy for collaborative roadmap construction. In: Proc. Int. Workshop on Algorithmic Foundations of Robotics (WAFR). Istanbul, Turkey (August 2014)

    Google Scholar 

  8. Doraiswamy, H., Natarajan, V.: Efficient algorithms for computing reeb graphs. Comput. Geom. Theory Appl. 42(6-7), 606–616 (Aug 2009), http://dx.doi.org/0.1016/j.comgeo.2008.12.003

  9. Foskey, M., Garber, M., Lin, M.C., Manocha, D.: A voronoi-based hybrid motion planner for rigid bodies. In: Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS). pp. 55–60 (2001)

    Google Scholar 

  10. Gayle, R., Sud, A., Lin, M.C., Manocha, D.: Reactive deformation roadmaps: Motion planning of multiple robots in dynamic environments. In: Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS) (2007)

    Google Scholar 

  11. Hatcher, A.: Algebraic Topology. Cambridge University Press (2001), http://www.math.cornell.edu/~hatcher/

  12. Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3d shapes. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. pp. 203–212. SIGGRAPH ’01, ACM, New York, NY, USA (2001), http://doi.acm.org/10.1145/383259.383282

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

    Google Scholar 

  14. Kalisiak, M., van de Panne, M.: Rrt-blossom: Rrt with a local flood-fill behavior. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. pp. 1237–1242 (May 2006)

    Google Scholar 

  15. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. International Journal of Robotics Research (IJRR) 30, 846–894 (2011)

    Google Scholar 

  16. Kuffner, J.J., LaValle, S.M.: RRT-connect: An efficient approach to single-query path planning. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA). pp. 995–1001 (2000)

    Google Scholar 

  17. Kurniawati, H., Hsu, D.: Workspace importance sampling for probabilistic roadmap planning. In: Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS). vol. 2, pp. 1618–1623 (sept-2 oct 2004)

    Google Scholar 

  18. Kurniawati, H., Hsu, D.: Workspace-based connectivity oracle - an adaptive sampling strategy for prm planning. In: Algorithmic Foundation of Robotics VII, pp. 35–51. Springer, Berlin/Heidelberg (2008), book contains the proceedings of the International Workshop on the Algorithmic Foundations of Robotics (WAFR), New York City, 2006

    Google Scholar 

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

    Google Scholar 

  20. Li, Y., Littlefield, Z., Bekris, K.E.: Sparse Methods for Efficient Asymptotically Optimal Kinodynamic Planning, pp. 263–282. Springer International Publishing, Cham (2015), http://dx.doi.org/10.1007/978-3-319-16595-0_16

  21. Lien, J.M., Pratt, E.: Interactive planning for shepherd motion (March 2009), the AAAI Spring Symposium

    Google Scholar 

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

    Google Scholar 

  23. McMahon, T., Thomas, S.L., Amato, N.M.: Reachable volume RRT. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA). pp. 2977–2984. Seattle, Wa. (May 2015)

    Google Scholar 

  24. 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, pp. 361–376. Springer Tracts in Advanced Robotics, Springer, Berlin/Heidelberg (2005), (WAFR ‘04)

    Google Scholar 

  25. Pascucci, V., Scorzelli, G., Bremer, P.T., Mascarenhas, A.: Robust on-line computation of reeb graphs: Simplicity and speed. ACM Trans. Graph. 26(3) (Jul 2007), http://doi.acm.org/10.1145/1276377.1276449

  26. Plaku, E., Kavraki, L., Vardi, M.: Motion planning with dynamics by a synergistic combination of layers of planning 26(3), 469–482 (June 2010)

    Google Scholar 

  27. Reeb, G.: Sur les points singuliers d’une forme de pfaff complement integrable ou d’une fonction numerique. Comptes Rendus Acad. Sciences Paris 222, 847–849 (1946)

    Google Scholar 

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

    Google Scholar 

  29. Rodriguez, S., Tang, X., Lien, J.M., Amato, N.M.: An obstacle-based rapidlyexploring random tree. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2006)

    Google Scholar 

  30. Si, H.: Tetgen, a delaunay-based quality tetrahedral mesh generator. ACM Trans. Math. Softw. 41(2), 11:1–11:36 (Feb 2015), http://doi.acm.org/10.1145/2629697

  31. Suh, C., Kim, B., Park, F.C.: The tangent bundle RRT algorithms for constrained motion planning. In: 13th World Congress in Mechanism and Machine Science (2011)

    Google Scholar 

  32. Wood, Z., Hoppe, H., Desbrun, M., Schröder, P.: Removing excess topology from isosurfaces. ACM Trans. Graph. 23(2), 190–208 (Apr 2004), http://doi.acm.org/10.1145/990002.990007

  33. Wood, Z.J., Schröder, P., Breen, D., Desbrun, M.: Semi-regular mesh extraction from volumes. In: Proceedings of the Conference on Visualization ’00. pp. 275–282. VIS ’00, IEEE Computer Society Press, Los Alamitos, CA, USA (2000), http://dl.acm.org/citation.cfm?id=375213.375254

  34. 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 

  35. Yershova, A., Jaillet, L., Simeon, T., Lavalle, S.M.: Dynamic-domain RRTs: Efficient exploration by controlling the sampling domain. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA). pp. 3856–3861 (April 2005)

    Google Scholar 

  36. Zhang, L., Manocha, D.: An efficient retraction-based RRT planner. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, USA

    Jory Denny

  2. Parasol Lab, Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Read Sandström, Andrew Bregger & Nancy M. Amato

Authors
  1. Jory Denny
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Read Sandström
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew Bregger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nancy M. Amato
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jory Denny .

Editor information

Editors and Affiliations

  1. Industrial Engineering and Operations Research, University of California, Berkeley, CA, USA

    Ken Goldberg

  2. Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA

    Pieter Abbeel

  3. Rutgers University, Piscataway, NJ, USA

    Kostas Bekris

  4. University of California, Berkeley, CA, USA

    Lauren Miller

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Denny, J., Sandström, R., Bregger, A., Amato, N.M. (2020). Dynamic Region-biased Rapidly-exploring Random Trees. In: Goldberg, K., Abbeel, P., Bekris, K., Miller, L. (eds) Algorithmic Foundations of Robotics XII. Springer Proceedings in Advanced Robotics, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-43089-4_41

Download citation

Publish with us

Policies and ethics

Search

Navigation