Abstract
Robot motions typically originate from an uninformed path sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision-testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling—in real time and with minimal information—the locations of obstacles, based on collision test results. We demonstrate up to a 780 % increase in paths surviving collision test.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, D. Vallejo, OBPRM: an obstacle-based PRM for 3D workspaces, in Workshop on the Algorithmic Foundations of Robotics, Houston, TX, USA, March (1998), pp. 155–168
W.H. Beyer (ed.), CRC Standard Mathematical Tables and Formulae (CRC Press, Boca Raton, 1991)
B. Burns, O. Brock, Information theoretic construction of probabilistic roadmaps, in International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA, Oct (2003)
B. Burns, O. Brock, Sampling-based motion planning using predictive models, in International Conference on Robotics and Automation, Barcelona, Spain, April (2005)
B. Burns, O. Brock, Single-query entropy-guided path planning, in International Conference on Robotics and Automation, Barcelona, Spain, April (2005)
B. Burns, O. Brock, Toward optimal configuration space sampling, in Robotics: Science and Systems, Cambridge, MA, USA, June (2005)
C. Green, A. Kelly, Toward optimal sampling in the space of paths, in International Symposium of Robotics Research, Hiroshima, Japan, Nov (2007)
P.D. Grünwald, A.P. Dawid, Game theory, maximum entropy, minimum discrepancy and robust bayesian decision theory. Ann. Stat. 32(4) (2004)
D. Hsu, T. Jiang, J. Reif, Z. Sun, The bridge test for sampling narrow passages with probabilistic roadmap planners, in International Conference on Robotics and Automation, Taipei, Taiwnn, Sept (2003)
D. Hsu, G. Sánchez-Ante, Z. Sun, Hybrid PRM sampling with a cost-sensitive adaptive strategy, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3885–3891 (2005)
D. Hsu, J.C. Latombe, H. Kurniawati, On the probabilistic foundations of probabilistic roadmap planning. Intl. J. Robot. Res. 25(7), 627–643 (2006)
L. Jaillet, A. Yershova, S.M. LaValle, T. Simeon, Adaptive tuning of the sampling domain for dynamic-domain RRTs, in International Conference on Intelligent Robots and Systems (2005)
E.T. Jaynes, Information theory and statistical mechanics. Phys. Rev. 106(4), 620–630 (1957)
R.A. Knepper, M.T. Mason, Empirical sampling of path sets for local area motion planning, in International Symposium of Experimental Robotics, Athens, Greece, July (2008)
R.A. Knepper, S.S. Srinivasa, M.T. Mason, An equivalence relation for local path sets, in Workshop on the Algorithmic Foundations of Robotics, Singapore, Dec (2010)
M. Morales, L. Tapia, R. Pearce, S. Rodriguez, N.M. Amato, A machine learning approach for feature-sensitive motion planning, in Workshop on the Algorithmic Foundations of Robotics, Utrecht/Zeist, The Netherlands, July (2004), pp. 361–376
A.F. van der Stappen, V. Boor, M.H. Overmars, The Gaussian sampling strategy for probabilistic roadmap planners, in International Conference on Robotics and Automation (1999), pp. 1018–1023
B. Vidakovic, Γ-Minimax: A Paradigm for Conservative Robust Bayesians, Lecture Notes in Statistics, vol. 152 (Springer, New York, 2000), pp. 241–259
Y. Yu, K. Gupta, An information theoretic approach to view point planning for motion planning of eye-in-hand systems, in International Symposium on Robotics (2000), pp. 306–311
Acknowledgment
This work is sponsored by the Defense Advanced Research Projects Agency. This work does not necessarily reflect the position or the policy of the Government. No official endorsement should be inferred.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Knepper, R.A., Mason, M.T. (2017). Realtime Informed Path Sampling for Motion Planning Search. In: Christensen, H., Khatib, O. (eds) Robotics Research . Springer Tracts in Advanced Robotics, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-29363-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-29363-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29362-2
Online ISBN: 978-3-319-29363-9
eBook Packages: EngineeringEngineering (R0)