Skip to main content
SpringerLink
Log in
Book cover

Robotics Research pp 563–579Cite as

A General Region-Based Framework for Collaborative Planning

  • Chapter
  • First Online:

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 3))

Abstract

Sampling-based planning is a common method for solving motion planning problems. However, this paradigm falters in difficult scenarios, such as narrow passages. In contrast, humans can frequently identify these challenges and can sometimes propose an approximate solution. A recent method called Region Steering takes advantage of this intuition by allowing a user to define regions in the workspace to weight the search space for probabilistic roadmap planners. In this work, we extend Region Steering into a generalized Region-Based framework that is suitable for any sampling-based planning approach. We explore three variants of our framework for graph-based, tree-based, and hybrid planning methods. We evaluate these variants in simulations as a proof of concept. Our results demonstrate the benefits of our framework in reducing overall planning time.

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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
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

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

    Google Scholar 

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

    Google Scholar 

  3. Boor, V., Overmars, M.H., van der Stappen, A.F.: The Gaussian sampling strategy for probabilistic roadmap planners. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), vol. 2, pp. 1018–1023 (1999)

    Google Scholar 

  4. 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. In: Proceedings of Annual Haifa Experimental Systems Conference (SYSTOR), pp. 1–10. ACM, New York, NY, USA (2010). doi:10.1145/1815695.1815713

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

    Google Scholar 

  6. 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 

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

    Article  MathSciNet  MATH  Google Scholar 

  8. 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 and Automation (ICRA), pp. 4420–4426 (2003)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  10. 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 

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

    Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. Kuffner, J.J., LaValle, S.M.: RRT-connect: an efficient approach to single-query path planning. In: Proceedings of IEEE International Conference on Robotics and Automatic (ICRA), pp. 995–1001 (2000)

    Google Scholar 

  15. KUKA: http://www.youbot-store.com

  16. Ladeveze, N., Fourquet, J.Y., Puel, B., Taix, M.: Haptic assembly and disassembly task assistance using interactive path planning. In: Virtual Reality Conference, 2009. VR 2009. IEEE, pp. 19–25 (2009)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Lien, J.M., Pratt, E.: Interactive planning for shepherd motion. The AAAI Spring Symposium (2009)

    Google Scholar 

  20. Lin, M.C.: Efficient collision detection for animation and robotics. Ph.D. thesis, University of California, Berkeley, CA (1993)

    Google Scholar 

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

    Article  Google Scholar 

  22. 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 

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

    Google Scholar 

  24. Plaku, E., Bekris, K.E., Chen, B.Y., Ladd, A.M., Kavraki, L.E.: Sampling-based roadmap of trees for parallel motion planning. IEEE Trans. Robot. Autom. (2005)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  27. Rodriguez, S., Tang, X., Lien, J.M., Amato, N.M.: An obstacle-based rapidly-exploring random tree. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2006)

    Google Scholar 

  28. Shi, K., Denny, J., Amato, N.M.: Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, P. R. China (2014)

    Google Scholar 

  29. 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 

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

    Article  Google Scholar 

  31. 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)

    Article  Google Scholar 

  32. Vargas Estrada, A., Lien, J.M., Amato, N.M.: Vizmo++: a visualization, authoring, and educational tool for motion planning. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 727–732 (2006)

    Google Scholar 

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

    Google Scholar 

  34. Zhang, L., Manocha, D.: An efficient retraction-based RRT planner. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2008)

    Google Scholar 

Download references

Acknowledgements

We would like to thank Brennen Taylor, a student at Texas A&M University, Ariana Ramirez, a student at Jimmy Carter Early College high school in La Joya, TX, USA, and Jonathon Colbert, a student at A&M Consolidated high school in College Station, TX, USA, for their participation in a summer research study on this topic in 2014.

This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-1423111, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11.

Jory Denny contributed to this research as a Ph.D. student at Texas A&M University. During this time, he was supported in part by an NSF Graduate Research Fellowship.

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 & 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. 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. Istituto Italiano di Tecnologia, Genova, Italy, University of Pisa, Pisa, Italy , Pisa, Italy

    Antonio Bicchi

  2. Inst. für Informatik, Albert-Ludwigs-Universität Freiburg Inst. für Informatik, Freiburg, Germany

    Wolfram Burgard

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Denny, J., Sandström, R., Amato, N.M. (2018). A General Region-Based Framework for Collaborative Planning. In: Bicchi, A., Burgard, W. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-60916-4_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60916-4_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60915-7

  • Online ISBN: 978-3-319-60916-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation