Abstract
In many robotics applications, the environment (robots and obstacles) often have very complex geometries. These result in expensive primitive computations such as collision detection which in turn, affect the overall performance of these applications. Approximating the geometry is a common approach to optimize computation. Unlike other applications of geometric approximation where it is applied to one space (usually obstacle space), we approximate both obstacle and free workspace with a set of geometric shape primitives that are completely contained within the space and represent its topology (skeleton). We use these “shape primitive skeletons” to improve collision detection performance in motion planning algorithms. Our results show that the use of shape primitive skeletons improves the performance of standard collision detection methods in motion planning problems by 20–70% in our 2D and 3D test environments regardless of motion planning strategy. We also show how the same shape primitive skeletons can be used with robots of different sizes to improve the performance of collision detection operation.
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
Cgal, Computational Geometry Algorithms Library (1997). https://www.cgal.org/
Amato, N.M., Wu, Y.: A randomized roadmap method for path and manipulation planning. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 113–120 (1996)
Bayani, H., Masouleh, M.T., Karimi, A., Cardou, P., Ebrahimi, M.: On the determination of the maximal inscribed ellipsoid in the wrench-feasible workspace of the cable-driven parallel robots. In: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp. 422–427 (2014)
Bialkowski, J., Otte, M., Frazzoli, E.: Free-configuration biased sampling for motion planning. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 1272–1279 (2013)
Bialkowski, J., Otte, M., Karaman, S., Frazzoli, E.: Efficient collision checking in sampling-based motion planning via safety certificates. Int. J. Robot. Res. 35(7), 767–796 (2016)
Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the Perception of Speech and Visual Form, pp. 362–380. MIT Press, Cambridge (1967)
Brock, O., Kavraki, L.: Decomposition-based motion planning: a framework for real-time motion planning in high-dimensional configuration places. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1469–1475 (2001)
Conway, J.H., Sloane, N.J.H.: Sphere Packings, Lattices and Groups, 3rd edn. Springer Science and Business Media, New York (1998)
Deits, R., Tedrake, R.: Computing large convex regions of obstacle-free space through semi-definite programming. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds.) Algorithmic Foundations of Robotics XI. Springer, Cham (2014)
Denny, J., Amato, N.M.: Toggle PRM: a coordinated mapping of C-free and C-obstacle in arbitrary dimension. In: Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds.) Algorithmic Foundations of Robotics X. Springer Tracts in Advanced Robotics, vol. 86, pp. 297–312. Springer, Heidelberg (2013)
Gottschalk, S., Lin, M.C., Manocha, D.: OBB-tree: a hierarchical structure for rapid interference detection. Comput. Graph. 30, 171–180 (1996). Proceedings of the SIGGRAPH 1996
Hubbard, P.M.: Approximating polyhedra with spheres for time-critical collision detection. ACM Trans. Graph. 15(3), 179–210 (1996)
Kallrath, J.: Packing ellipsoids into volume-minimizing rectangular boxes. J. Global Optim. 67, 151–185 (2017)
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)
Klosowski, J., Held, M., Mitchell, J.S.B., Zikan, K., Sowizral, H.: Efficient collision detection using bounding volume hierarchies of \(k\)-DOPs. IEEE Trans. Vis. Comput. Graph. 4(1), 21–36 (1998)
Lacevic, B., Osmankovic, D., Ademovic, A.: Burs of free C-space: a novel structure for path planning. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 70–76 (2016)
Larsen, E., Gottschalk, S., Lin, M.C., Manocha, D.: Fast distance queries with rectangular swept sphere volumes. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 4, pp. 3719–3726 (2000)
LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)
Li, S., Zhao, J., Lu, P., Xie, Y.: Maximum packing densities of basic 3d objects. Chin. Sci. Bull. 55(2), 114–119 (2010)
Schneider, D., Schömer, E., Wolpert, N.: Collision detection for 3d rigid body motion planning with narrow passages. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 4365–4370 (2017)
Schwarzer, F., Saha, M., Latombe, J.C.: Exact collision checking of robot paths. In: Boissonnat, J.D., Burdick, J., Goldberg, K., Hutchinson, S. (eds.) Algorithmic Foundations of Robotics V, pp. 25–41. Springer, Heidelberg (2004)
Stolpner, S., Kry, P., Siddiqi, K.: Medial spheres for shape approximation. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1234–1240 (2012)
Tagliasacchi, A., Alhashim, I., Olson, M., Zhang, H.: Mean curvature skeletons. Comput. Graph. Forum 31(5), 1735–1744 (2012)
Tu, C., Yu, L.: Research on collision detection algorithm based on AABB-OBB bounding volume. In: 2009 First International Workshop on Education Technology and Computer Science, vol. 1, pp. 331–333 (2009)
Weller, R., Zachmann, G.: Inner sphere trees for proximity and penetration queries. In: Robotics: Science and Systems, vol. 2 (2009)
Yang, Y., Brock, O.: Adapting the sampling distribution in PRM planners based on an approximated medial axis. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 5, pp. 4405–4410 (2004)
Yershova, A., Jaillet, L., Simeon, T., Lavalle, S.M.: Dynamic-domain RRTs: efficient exploration by controlling the sampling domain. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3856–3861 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ghosh, M., Thomas, S., Amato, N.M. (2020). Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons. In: Morales, M., Tapia, L., Sánchez-Ante, G., Hutchinson, S. (eds) Algorithmic Foundations of Robotics XIII. WAFR 2018. Springer Proceedings in Advanced Robotics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-44051-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-44051-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44050-3
Online ISBN: 978-3-030-44051-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)