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A family of skeletons for motion planning and geometric reasoning applications

Published online by Cambridge University Press:  12 October 2011

Ata A. Eftekharian
Affiliation:
Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut, USA
Horea T. Ilieş*
Affiliation:
Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut, USA
*
Reprint requests to: Horea T. Ilieş, Department of Mechanical Engineering, 366 United Technologies Building, 191 Auditorium Road, Unit 3139, University of Connecticut, Storrs, CT 06269-3139, USA. E-mail: [email protected]

Abstract

The task of planning a path between two spatial configurations of an artifact moving among obstacles is an important problem in practically all geometrically intensive applications. Despite the ubiquity of the problem, the existing approaches make specific limiting assumptions about the geometry and mobility of the obstacles, or those of the environment in which the motion of the artifact takes place. We present a strategy to construct a family of paths, or roadmaps, for two- and three-dimensional solids moving in an evolving environment that can undergo drastic topological changes. Our approach is based on a potent paradigm for constructing geometric skeletons that relies on constructive representations of shapes with R-functions that operate on real-valued half-spaces as logic operations. We describe a family of skeletons that have the same homotopy as that of the environment and contains the medial axis as a special case. Of importance, our skeletons can be designed so that they are “attracted to” or “repulsed by” prescribed spatial sites of the environment. Moreover, the R-function formulation suggests the new concept of a medial zone, which can be thought of as a “thick” skeleton with significant applications for motion planning and other geometric reasoning applications. Our approach can handle problems in which the environment is not fully known a priori, and intrinsically supports local and parallel skeleton computations for domains with rigid or evolving boundaries. Furthermore, our path planning algorithm can be implemented in any commercial geometric kernel, and has attractive computational properties. The capability of the proposed technique are explored through several examples designed to simulate highly dynamic environments.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Amenta, N., Choi, S., & Kolluri, R.K. (2001). The power crust, unions of balls, and the medial axis transform. Computational Geometry: Theory and Applications 19, 127153.CrossRefGoogle Scholar
Attali, D., Boissonnat, J.-D., & Edelsbrunner, H. (2008). Stability and computation of medial axes—a state-of-the-art report. In Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration, Mathematics and Visualization (Möller, T., Hamann, B., & Russel, R., Eds.). Berlin: Springer–Verlag.Google Scholar
Blum, H. (1967). A transformation for extracting new descriptions of shape. Proc. Models for the Perception of Speech and Visual Form, pp. 362380.Google Scholar
Blum, H., & Nagel, R. (1978). Shape description using weighted symmetric axis features. Pattern Recognition 10, 167180.CrossRefGoogle Scholar
Bookstein, F. (1979). The line-skeleton. Computer Graphics and Image Processing 11(2), 123137.CrossRefGoogle Scholar
Bovik, A.C. (2005). Handbook of Image and Video Processing (Communications, Networking and Multimedia). Orlando, FL: Academic Press.Google Scholar
Brandt, J.W., & Algazi, V.R. (1992). Continuous skeleton computation by Voronoi diagram. CVGIP: Image Understanding 55(3), 329338.CrossRefGoogle Scholar
Buchele, S.F., & Crawford, R.H. (2003). Three-dimensional halfspace constructive solid geometry tree construction from implicit boundary representations. Proc. 8th ACM Symp. Solid Modeling and Applications, pp. 135144. New York: ACM Press.CrossRefGoogle Scholar
Canny, J. (1993). Computing roadmaps of general semi-algebraic sets. Computer Journal 36(5), 504514.CrossRefGoogle Scholar
Cao, L., & Liu, J. (2008). Computation of medial axis and offset curves of curved boundaries in planar domain. Computer-Aided Design 40(4), 465475.CrossRefGoogle Scholar
Chazal, F., & Soufflet, R. (2004). Stability and finiteness properties of medial axis and skeleton. Journal of Dynamical and Control Systems 10(2), 149170.CrossRefGoogle Scholar
Chen, J., Shapiro, V., Suresh, K., & Tsukanov, I. (2007). Shape optimization with topological changes and parametric control. International Journal for Numerical Methods in Engineering 71, 313346.CrossRefGoogle Scholar
Choi, H.I., Choi, S.W., & Moon, H.P. (1997). Mathematical theory of medial axis transform. Pacific Journal of Mathematics 1(181), 5788.CrossRefGoogle Scholar
Choset, H. (2000). Coverage of known spaces: the boustrophedon cellular decomposition. Autonomous Robots 9, 247253.CrossRefGoogle Scholar
Culver, T., Keyser, J., & Manocha, D. (2004). Exact computation of the medial axis of a polyhedron. Computer Aided Geometric Design 21(1), 6598.CrossRefGoogle Scholar
Damon, J. (2005). Determining the geometry of boundaries of objects from medial data. International Journal of Computer Vision 63, 4564.CrossRefGoogle Scholar
Dasgupta, S., Papadimitriou, C.H., & Vazirani, U. (2008). Algorithms. New York: McGraw–Hill.Google Scholar
Diaz de León, S.J.L., & Sossa, A.J.H. (1998). Automatic path planning for a mobile robot among obstacles of arbitrary shape. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(3), 467472.CrossRefGoogle Scholar
Dey, T.K., Woo, H., & Zhao, W. (2003). Approximate medial axis for CAD models. Proc. 8th ACM Symp. Solid Modeling and Applications: ISM ‘03, pp. 280285. New York: ACM.CrossRefGoogle Scholar
Dijkstra, E.W. (1959). A note on two problems in connection with graphs. Numerische Mathematik 1, 269271.CrossRefGoogle Scholar
Dobkin, D., Guibas, L., Hershberger, J., & Snoeyink, J. (1988). An efficient algorithm for finding the CSG representation of a simple polygon. Proc. 15th Annual Conf. Computer Graphics and Interactive Techniques: SIGGRAPH ‘88, pp. 3140. New York: ACM.CrossRefGoogle Scholar
Du, H., & Qin, H. (2004). Medial axis extraction and shape manipulation of solid objects using parabolic PDEs. Proc. 9th ACM Symp. Solid Modeling and Applications: SM ‘04, pp. 2535. Aire-la-Ville, Switzerland: Eurographics Association.Google Scholar
Eftekharian, A., & Ilieş, H. (2009). Distance functions and skeletal representations of rigid and non-rigid planar shapes. Computer-Aided Design 41(12), 865876.CrossRefGoogle Scholar
Eftekharian, A., & Ilieş, H. (2010). Medial zones: formulation, properties and applications. Technical Report, University of Connecticut.Google Scholar
Foskey, M., Garber, M., Lin, M.C., & Manocha, D. (2001). A Voronoi-based hybrid motion planner. Proc. IEEE/RSJ Int. Conf. Intelligent Robots & Systems, pp. 5560.CrossRefGoogle Scholar
Fraichard, T. (1993). Dynamic trajectory planning with dynamic constraints: a “state-time space” approach. Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems ‘93: IROS ‘93, Vol. 2, pp. 13931400.CrossRefGoogle Scholar
Garber, M., & Lin, M.C. (2002). Constraint-based motion planning for virtual prototyping. Proc. 7th ACM Symp. Solid Modeling and Applications: SMA ‘02, pp. 257264. New York: ACM.CrossRefGoogle Scholar
Geraerts, R., & Overmars, M.H. (August 2007). Creating high-quality paths for motion planning. International Journal of Robotics Research 26(8), 845863.CrossRefGoogle Scholar
Goh, W.-B. (2008). Strategies for shape matching using skeletons. Computer Vision and Image Understanding 110, 326345.CrossRefGoogle Scholar
Gomes, J., & Faugeras, O. (2000). Reconciling distance functions and level sets. Journal of Visual Communication and Image Representation 11(2), 209223.CrossRefGoogle Scholar
Hoff, K. III, Culver, T., Keyser, J., Lin, M., & Manocha, D. (2000). Interactive motion planning using hardware-accelerated computation of generalized Voronoi diagrams. Proc. IEEE Int. Conf. Robotics and Automation, 2000: ICRA’00, Vol. 3.Google Scholar
Ishikawa, S. (1991). A method of indoor mobile robot navigation by using fuzzy control. Proc. IEEE/RSJ Int. Workshop on Intelligent Robots and Systems: IROS ‘91, Vol. 2, pp. 10131018.Google Scholar
Jiang, K., Seneviratne, L.D., & Earles, S.W.E. (1999). A shortest path based path planning algorithm for nonholonomic mobile robots. Journal of Intelligent and Robotic Systems 24(4), 347366.CrossRefGoogle Scholar
Kallman, M., & Mataric, M. (2004). Motion planning using dynamic roadmaps. Proc. 2004 IEEE Int. Conf. Robotics and Automation: ICRA ‘04, Vol. 5, pp. 43994404.CrossRefGoogle Scholar
Kavraki, L.E., Vestka, P., Latombe, J.-C., & Overmars, M.H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation 12, 566580.CrossRefGoogle Scholar
Kimia, B.B., & Tamrakar, A. (2002). The role of propagation and medial geometry in human vision. Proc. 2nd Int. Workshop on Biologically Motivated Computer Vision: BMCV ‘02, pp. 219229. London: Springer–Verlag.CrossRefGoogle Scholar
Kimmel, R., Shaked, D., Kiryati, N., & Bruckstein, A. (1995). Skeletonization via distance maps and level sets. Computer Vision and Image Understanding 62(3), 382391.CrossRefGoogle Scholar
Latombe, J.-C. (1991). Robot Motion Planning. Norwell, MA: Kluwer Academic.CrossRefGoogle Scholar
LaValle, S.M. (2006). Planning Algorithms. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Lee, J.Y., & Choset, H. (May 2005). Sensor-based planning for a rod-shaped robot in three dimensions. International Journal of Robotics Research 24, 343383.CrossRefGoogle Scholar
Lieutier, A. (2003). Any open bounded subset of rn has the same homotopy type than its medial axis. In Proc. 8th ACM Symp. Solid Modeling and Applications: SM ‘03, pp. 6575. New York: ACM.CrossRefGoogle Scholar
Lingelbach, F. (2004). Path planning using probabilistic cell decomposition. Proc. IEEE Int. Conf. Robotics & Automation.CrossRefGoogle Scholar
Liu, T.-L., Geiger, D., & Kohn, R.V. (1998). Representation and self-similarity of shapes. Proc. 6th Int. Conf. Computer Vision: ICCV ‘98, pp. 1129. Washington, DC: IEEE Computer Society.Google Scholar
Luo, Z., Tong, L., Wang, M.Y., & Wang, S. (2007). Shape and topology optimization of compliant mechanisms using a parameterization level set method. Journal of Computer Physics 227, 680705.CrossRefGoogle Scholar
Masehian, E., & Katebi, Y. (2007). Robot motion planning in dynamic environments with moving obstacles and target. International Journal of Mechanical Systems Science and Engineering 1(1), 2025.Google Scholar
Neus, M., & Maouche, S. (2005). Motion planning using the modified visibility graph. Proc. IEEE Int. Conf. Systems Man and Cybernetics, Vol. 4, pp. 651655.Google Scholar
Pizer, S.M., Siddiqi, K., Székely, G., Damon, J.N., & Zucker, S.W. (2003). Multiscale medial loci and their properties. International Journal of Computer Vision 55(2–3), 155179.CrossRefGoogle Scholar
Quadros, W., Shimada, K., & Owen, S. (2004). Skeleton-based computational method for the generation of a 3D finite element mesh sizing function. Engineering with Computers 20(3), 249264.CrossRefGoogle Scholar
Ramamurthy, R., & Farouki, R.T. (1999). Voronoi diagram and medial axis algorithm for planar domains with curved boundaries—II: detailed algorithm description. Journal of Computers and Applied Mathematics 102(2), 253277.CrossRefGoogle Scholar
Requicha, A. (1980). Representations for rigid solids: theory, methods and systems. Computing Surveys 12(4), 437463.CrossRefGoogle Scholar
Rimon, E., & Koditschek, D. (1992). Exact robot navigation using artificial potential functions. IEEE Transactions on Robotics and Automation 8(5), 501518.CrossRefGoogle Scholar
Sacks, E., Pisula, C., & Joskowicz, L. (1999). Visualizing 3d configuration spaces for mechanical design. IEEE Computer Graphics and Applications 19(5), 5053.CrossRefGoogle Scholar
Sampl, P. (2000). Semi-structured mesh generation based on medial axis. Proc. IMR, pp. 2132.Google Scholar
Sampl, P. (2001). Medial axis construction in three dimensions and its application to mesh generation. Engineering with Computers 17, 234248.CrossRefGoogle Scholar
Sebastian, T., Klein, P., & Kimia, B. (2004). Recognition of shapes by editing their shock graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 550571.CrossRefGoogle ScholarPubMed
Shah, J. (2005). Gray skeletons and segmentation of shapes. Computer Vision and Image Understanding 99, 96109.CrossRefGoogle Scholar
Shaham, A., Shamir, A., & Cohen-Or, D. (2004). Medial axis based solid representation. Proc. 9th ACM Symp. Solid Modeling and Applications.Google Scholar
Shapiro, V. (1991). Theory of R-Functions and Applications: A Primer, Technical Report TR91-1219. Ithaca, NY: Cornell University, Computer Science Department.Google Scholar
Shapiro, V. (2001). A convex deficiency tree algorithm for curved polygons. International Journal of Computational Geometry and Applications 11(2), 215238.CrossRefGoogle Scholar
Shapiro, V. (2007). Semi-analytic geometry with R-functions. Acta Numerica 18, 239303.CrossRefGoogle Scholar
Shapiro, V., & Vossler, D.L. (1991). Construction and optimization of CSG representations. Computer-Aided Design 23(1), 420.CrossRefGoogle Scholar
Shapiro, V., & Vossler, D.L. (1993). Separation for boundary to CSG conversion. ACM Transactions on Graphics 12(1), 3555.CrossRefGoogle Scholar
Sherbrooke, E., Patrikalakis, N., & Brisson, E. (1995). Computation of the medial axis transform of 3-D polyhedra. Proc. 3rd ACM Symp. Solid Modeling and Applications, pp. 187200. New York: ACM.CrossRefGoogle Scholar
Siddiqi, K., Bouix, S., Tannenbaum, A., & Zucker, S.W. (2002). Hamilton–Jacobi skeletons. International Journal of Computer Vision 48(3), 15231.CrossRefGoogle Scholar
Siddiqi, K., Shokoufandeh, A., Dickinson, S.J., & Zucker, S.W. (1999). Shock graphs and shape matching. International Journal of Computer Vision 35(1), 1332.CrossRefGoogle Scholar
Snavely, N., Garg, R., Seitz, S., & Szeliski, R. (2008). Finding paths through the world's photos. ACM Transactions on Graphics 27(3), 15.CrossRefGoogle Scholar
Suresh, K. (2003). Automating the CAD/CAE dimensional reduction process. Proc. 8th ACM Symp. Solid Modeling and Applications: SM ‘03, pp. 7685. New York: ACM.CrossRefGoogle Scholar
Takaoka, T. (2005). An O(n3 log log n/log n) time algorithm for the all-pairs shortest path problem. Information Processing Letters 96(5), 155161.CrossRefGoogle Scholar
Tor, S.B., & Middleditch, A.E. (1984). Convex decomposition of simple polygons. ACM Transactions on Graphics (TOG) 3(4), 244265.CrossRefGoogle Scholar
van den Berg, J., & Overmars, M. (2005). Roadmap-based motion planning in dynamic environments. IEEE Transactions on Robotics 21(5), 885897.CrossRefGoogle Scholar
van Eede, M., Macrini, D., Telea, A., Sminchisescu, C., & Dickinson, S. (2006). Canonical skeletons for shape matching. Proc. 18th Int. Conf. Pattern Recognition ICPR 2006, Vol. 2, pp. 6469.CrossRefGoogle Scholar
Wilmarth, S.A., Amato, N.M., & Stiller, P.F. (1999). Motion planning for a rigid body using random networks on the medial axis of the free space. Proc. 15th Annual Symp. Computational Geometry: SCG ‘99, pp. 173180. New York: ACM.CrossRefGoogle Scholar
Xidias, E.K., Azariadis, P.N., & Aspragathos, N.A. (2007). Two-dimensional motion-planning for non-holonomic robots using bump-surface concept. Computing 79, 109118.CrossRefGoogle Scholar