Journal of Intelligent and Robotic Systems

, Volume 53, Issue 3, pp 223–245 | Cite as

Motion Planning for Haptic Guidance

  • Jan Rosell
  • Carlos Vázquez
  • Alexander Pérez
  • Pedro Iñiguez


Haptic devices allow a user to feel either reaction forces from virtual interactions or reaction forces reflected from a remote site during a bilateral teleoperation task. Also, guiding forces can be exerted to train the user in the performance of a virtual task or to assist him/her to safely teleoperate a robot. The generation of guiding forces relies on the existence of a motion plan that provides the direction to be followed to reach the goal from any free configuration of the configuration space (\({\mathcal C}\)-space). This paper proposes a method to obtain such a plan that interleaves a sampling-based exploration of \({\mathcal C}\)-space with an efficient computation of harmonic functions. A deterministic sampling sequence (with a bias based on harmonic function values) is used to obtain a hierarchical cell decomposition model of \({\mathcal C}\)-space. A harmonic function is iteratively computed over the partially known model using a novel approach. The harmonic function is the navigation function used as motion plan. The approach has been implemented in a planner (called the Kautham planner) that, given an initial and a goal configuration, provides: (a) a channel of cells connecting the cell that contains the initial configuration with the cell that contains the goal configuration; (b) two harmonic functions over the whole \({\mathcal C}\)-space, one that guides motions towards the channel and another that guides motions within the channel towards the goal; and (c) a path computed over a roadmap built with the free samples of the channel. The harmonic functions and the solution path are then used to generate the guiding forces for the haptic device. The planning approach is illustrated with examples on 2D and 3D workspaces.


Haptic guidance Feedback motion planning Sampling-based methods Deterministic sampling Harmonic functions 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Basdogan, C., Kiraz, A., Bukusoglu, I., Varol, A., Doganay, S.: Haptic guidance for improved task performance in steering microparticles with optical tweezers. Opt. Express 15(18), 11616–11621 (2007)CrossRefGoogle Scholar
  2. 2.
    Bohlin, R., Kavraki, L.: Path planning using lazy PRM. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, vol. 1, pp. 521–528. IEEE, Piscataway (2000)Google Scholar
  3. 3.
    Boor, V., Overmars, M.H., van der Stappen, A.F.: The gaussian sampling strategy for probabilistic roadmap planners. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 1018–1023. IEEE, Piscataway (1999)Google Scholar
  4. 4.
    Burns, B., Brock, O.: Toward optimal configuration space sampling. In: Proceedings of Robotics: Science and Systems, Cambridge (June 2005)Google Scholar
  5. 5.
    Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., et al.: Cell decompositions. In: Principles of Robot Motion, pp. 161–196. MIT, Cambridge (2005)Google Scholar
  6. 6.
    Dale, L.K., Amato, N.M.: Probabilistic roadmaps - putting it all together. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 1940–1947. IEEE, Piscataway (2002)Google Scholar
  7. 7.
    Elhajj, I., Xi, N., Fung, W.K., Liu, Y.H., Li, W.J., Kaga, T., et al.: Haptic information in internet-based teleoperation. IEEE/ASME Trans. Mechatron. 6(3), 295–304 (2001)CrossRefGoogle Scholar
  8. 8.
    Feygin, D., Keehner, M., Tendick, F.: Haptic guidance: experimental evaluation of a haptic training method for a perceptual motor skill. In: HAPTICS’02: Proc. of the 10th Symp. on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 40–47. IEEE, Piscataway (2002)CrossRefGoogle Scholar
  9. 9.
    Font, I., Weiland, S., Franken, M., Steinbuch, M., Rovers, L.: Haptic feedback designs in teleoperation systems for minimal invasive surgery. In: IEEE Int. Conf. on Systems, Man and Cybernetics, vol. 3, pp. 2513–2518. IEEE, Piscataway (2004)Google Scholar
  10. 10.
    Galeano, D., Payandeh, S.: Artificial and natural force constraints in haptic-aided path planning. In: IEEE Int. Workshop on Haptic Audio Visual Environments and their Applications, pp. 45–50. IEEE, Piscataway (2005)CrossRefGoogle Scholar
  11. 11.
    Geraerts, R., Overmars, M.H.: Sampling and node adding in probabilistic roadmap planners. Robot. Auton. Syst. 54(2), 165–173 (2006)CrossRefGoogle Scholar
  12. 12.
    Gottschalk, S., Lin, M.C., Manocha, D.: OBBTree: a hierarchical structure for rapid interference detection. Comput. Graph. 30, 171–180 (1996) (Annual Conference Series)Google Scholar
  13. 13.
    Hageman, L.A., Young, D.M.: Applied Iterative Methods. Academic, London (1981)MATHGoogle Scholar
  14. 14.
    Halton, J.: On the effciency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numer. Math. 2, 84–90 (1960)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Hsu, D., Jiang, T., Reif, J., Sun, Z.: The bridge test for sampling narrow passages with probabilistic roadmap planners. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 4420–4426. IEEE, Piscataway (2003)Google Scholar
  16. 16.
    Hsu, D., Latombe, J.-C., Kurniawati, H.: On the probabilistic foundations of probabilistic roadmap planning. Int. J. Rob. Res. 25(7), 627–643 (2006)CrossRefGoogle Scholar
  17. 17.
    Hsu, D., Sanchez-Ante, G., Sun, Z.: Hybrid PRM sampling with a cost-sensitive adaptive strategy. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 3874–3880. IEEE, Piscataway (2005)CrossRefGoogle Scholar
  18. 18.
    Kavraki, L.E., Kolountzakis, M.N., Latombe, J.-C.: Analysis of probabilistic roadmaps for path planning. IEEE Trans. Robot. Autom. 14(1), 166–171 (1998)CrossRefGoogle Scholar
  19. 19.
    Kavraki, L.E., Latombe, J.-C.: Randomized preprocessing of configuration for fast path planning. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, vol. 3, pp. 2138–2145. IEEE, Piscataway (1994)Google Scholar
  20. 20.
    Kavraki, L.E., Svestka, P., Latombe, J.-C., Overmars, M.K.: Probabilistic roadmaps for path planning in high - dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)CrossRefGoogle Scholar
  21. 21.
    Kazemi, M., Mehrandezh, M., Gupta, K.: An incremental harmonic function-based probabilistic roadmap approach to robot path planning. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 2136–2141. IEEE, Piscataway (2005)CrossRefGoogle Scholar
  22. 22.
    Khatib, O.: Real-time obstacle aboidance for manipulators and mobile robots. Int. J. Rob. Res. 5(1), 90–98 (1986)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Kuffner, J.J., LaValle, S.M.: RRT-connect: an efficient approach to single-query path planning. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 995–1001. IEEE, Piscataway (2000)Google Scholar
  24. 24.
    Kurniawati, H., Hsu, D.: Workspace-based connectivity oracle: an adaptive sampling strategy for PRM planning. In: Akella, S. et al. (eds.) Algorithmic Foundations of Robotics VII. Springer, Heidelberg (2006)Google Scholar
  25. 25.
    Latombe, J.-C.: Potential field methods. In: Robot Motion Planning, pp. 296–355. Kluwer, Boston (1991)Google Scholar
  26. 26.
    Latombe, J.-C.: Approximate cell decomposition. In: Robot Motion Planning, pp. 248–294. Kluwer, Boston (1991)Google Scholar
  27. 27.
    LaValle, S.M.: Feedback motion planning. In: Planning Algorithms, pp. 367–430. Cambridge University Press, Cambridge (2006)Google Scholar
  28. 28.
    LaValle, S.M., Branicky, M.S., Lindemann, S.R.: On the relationship between classical grid search and probabilistic roadmaps. Int. J. Rob. Res. 23(7–8), 673–692 (2004)CrossRefGoogle Scholar
  29. 29.
    Leven, P., Hutchinson, S.: Using manipulability to bias sampling during the construction of probabilistic roadmaps. IEEE Trans. Robot. Autom. 19(6), 1020–1026 (2003)CrossRefGoogle Scholar
  30. 30.
    Lindemann, S.R., LaValle, S.M.: Incremental low-discrepancy lattice methods for motion planning. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 2920–2927. IEEE, Piscataway (2003)Google Scholar
  31. 31.
    Lindemann, S.R., LaValle, S.M.: Current issues in sampling-based motion planning. In: Proc. 8th Int. Symp. on Robotics Research. Springer, Heidelberg (2004)Google Scholar
  32. 32.
    Lindemann, S.R., Yershova, A., LaValle, S.M.: Incremental grid sampling strategies in robotics. In: Proc. of the Sixth Int. Workshop on the Algorithmic Foundations of Robotics, pp 297–312, Utrecht, 11–13 July 2004Google Scholar
  33. 33.
    Lingelbach, F.: Path planning using probabilistic cell decomposition. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp 467–472. IEEE, Piscataway (2004)Google Scholar
  34. 34.
    Otaduy, M. A., Lin, M.C.: A modular haptic rendering algorithm for stable and transparent 6-dof manipulation. IEEE Trans. Robot. 22(4), 751–762 (2006)CrossRefGoogle Scholar
  35. 35.
    Prestes, E., Engel, P.M., Trevisan, M., Idiart, M.A.P.: Exploration method using harmonic functions. Robot. Auton. Syst. 40, 25–42 (2002)CrossRefGoogle Scholar
  36. 36.
    Rosell, J., Iñiguez, P.: Path planning using harmonic functions and probabilistic cell decomposition. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp 1815–1820. IEEE, Piscataway (2005)Google Scholar
  37. 37.
    Rosell, J., Roa, M., Pérez, A., García, F.: A general deterministic sequence for sampling d-dimensional configuration spaces. J. Intell. Robot. Syst. 50(4), 361–374 (2007)MATHCrossRefGoogle Scholar
  38. 38.
    Rosell, J., Vázquez, C., Pérez, A.: Cspace decomposition using deterministic sampling and distances. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp 15–20. IEEE, Piscataway (2007)CrossRefGoogle Scholar
  39. 39.
    Ruspini, D.C., Kolarov, K., Khatib, O.: The haptic display of complex graphical environments. In: Proc. of the 24th annual Conf. on Computer graphics and Interactive Techniques, pp 345–352. ACM, New York (1997)CrossRefGoogle Scholar
  40. 40.
    Souccar, K., Coelho, J., Connolly, C., Grupen, R.: Harmonic functions for path planning and control. In: Practical Motion Planning in Robotics, pp 277–301. Wiley, New York (1998)Google Scholar
  41. 41.
    Srinivasan, M.A., Basdogan, C.: Haptics in virtual environments: taxonomy, research status, and challenges. Comput. Graph. 21(4), 393–404 (1997)CrossRefGoogle Scholar
  42. 42.
    Varol, A., Gunev, I., Basdogan, C.: A virtual reality toolkit for path planning and manipulation at nano-scale. In: 14th Symp. on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp 485–489. ASME, New York (2006)CrossRefGoogle Scholar
  43. 43.
    Vázquez, C., Rosell, J.: Haptic guidance based on harmonic functions for the execution of teleoperated assembly tasks. In: Preprints of the 2007 IFAC Workshop on Intelligent Assembly and Disassembly, pp 88–93, Alicante, 23–25 May 2007Google Scholar
  44. 44.
    Wilmarth, S.A., Amato, N.M., Stiller, P.F.: MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp 1024–1031. IEEE, Piscataway (1999)Google Scholar
  45. 45.
    Yang, L., LaValle, S.M.: The sampling-based neighborhood graph: An approach to computing and executing feedback motion strategies. IEEE Trans. Robot. Autom. 20(3), 419–432 (2004)CrossRefGoogle Scholar
  46. 46.
    Zhang, L., Kim, Y.J., Manocha, D.: A hybrid approach for complete motion planning. In: Accepted to IEEE/RSJ Int. Conf. on Intelligent Robots and Systems. IEEE, Piscataway (2007)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Jan Rosell
    • 1
  • Carlos Vázquez
    • 1
  • Alexander Pérez
    • 1
  • Pedro Iñiguez
    • 2
  1. 1.Institute of Industrial and Control Engineering (IOC-UPC)BarcelonaSpain
  2. 2.Department of ElectronicsElectrics and Automatic Engineering (URV)TarragonaSpain

Personalised recommendations