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.
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References
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)
Lien, J.M., Pratt, E.: Interactive planning for shepherd motion. In: The AAAI Spring Symposium, March 2009
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)
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
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)
LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)
Hsu, D., Latombe, J.C., Kurniawati, H.: On the probabilistic foundations of probabilistic roadmap planning. Int. J. Robot. Res. 25, 627–643 (2006)
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)
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)
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)
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)
Taïx, M., Flavigné, D., Ferré, E.: Human interaction with motion planning algorithm. J. Intell. Robot. Syst. 67(3–4), 285–306 (2012)
Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22(10), 560–570 (1979)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Hokayem, P.F., Spong, M.W.: Bilateral teleoperation: an historical survey. Automatica 42, 2035–2057 (2006)
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)
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)
Amato, N.M.: Motion planning benchmarks http://parasol.tamu.edu/groups/amatogroup/benchmarks/
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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