Abstract
Assuming that a robot trajectory is given from a high-level planning or learning mechanism, it needs to be adapted to react to dynamic environment changes. In this article we propose a novel approach to deform trajectories while keeping their local shape similar, which is based on the discrete Laplace–Beltrami operator. The approach can be readily extended and covers multiple deformation techniques including fixed waypoints that must be passed, positional constraints for collision avoidance or a cooperative manipulation scheme for the coordination of multiple robots. Due to its low computational complexity it allows for real-time trajectory deformation both on local and global scale and online adaptation to changed environmental constraints. Simulations illustrate the straightforward combination of the proposed approach with other established trajectory-related methods like artificial potential fields or prioritized inverse kinematics. Experiments with the HRP-4 humanoid successfully demonstrate the applicability in complex daily-life tasks.












Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Arun, K. S., Huang, T. S., & Blostein, S. D. (1987). Least-squares fitting of two 3-d point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5), 698–700.
Billard, A., Calinon, S., Dillmann, R., & Schaal, S. (2008). Robot programming by demonstration. In B. Siciliano & O. Khatib (Eds.), Springer handbook of robotics (pp. 1371–1394). Berlin: Springer.
Botsch, M., & Kobbelt, L. (2004a). An intuitive framework for real-time freeform modeling. ACM Transactions on Graphics, 23(3), 630–634.
Botsch, M., & Kobbelt, L. (2004b). A remeshing approach to multiresolution modeling. In ACM Special Interest Group on Graphics and Interactive Techniques (pp. 185–192).
Brock, O., & Khatib, O. (2002). Elastic strips: A framework for motion generation in human environments. International Journal of Robotics Research, 21(12), 1031–1052.
Desbrun, M., Meyer, M., Schröder, P., & Barr, A.H. (1999). Implicit fairing of irregular meshes using diffusion and curvature flow. In ACM Special Interest Group on Graphics and Interactive Techniques (pp. 317–324).
Dierkes, U., Hildebrandt, S., & Sauvigny, F. (2010). Minimal surfaces (p. 339). Berlin: Springer.
Do-Carmo, M. P. (1976). Differential geometry of curves and surfaces. Englewood Cliffs: Prentice Hall.
Eck, M., DeRose, T., Duchamp, T., Hoppe, H., Lounsbery, M., & Stuetzle, W. (1995). Multiresolution analysis of arbitrary meshes. In ACM Special Interest Group on Graphics and Interactive Techniques (pp. 173–182).
Flash, T., & Hogan, N. (1985). The coordination of arm movements: An experimentally confirmed mathematical model. Journal of Neuroscience, 5(7), 1688–1703.
Hilario, L., Montés, N., Mora, M.C., & Falcó, A. (2011). Real-time bézier trajectory deformation for potential fields planning methods. In IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1567–1572).
Hoffmann, H., Pastor, P., Park, D.H., & Schaal, S. (2009). Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance. In IEEE International Conference on Robotics and Automation (pp. 2587–2592).
Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. International Journal of Robotics Research, 30(7), 846–894.
Karni, Z., & Gotsman, C. (2000). Spectral compression of mesh geometry. In ACM Special Interest Group on Graphics and Interactive Techniques (pp. 279–286).
Kilner, J., Hamilton, A Fd C, & Blakemore, S. J. (2007). Interference effect of observed human movement on action is due to velocity profile of biological motion. Social Neuroscience, 2(3–4), 158–66.
Kobbelt, L., Bareuther, T., & Seidel, H. P. (2000). Multiresolution shape deformations for meshes with dynamic vertex connectivity. Computer Graphics Forum, 19(3), 249–260.
Kupferberg, A., Glasauer, S., Huber, M., Rickert, M., Knoll, A., & Brandt, T. (2011). Biological movement increases acceptance of humanoid robots as human partners in motor interaction. AI & Society, 26(4), 339–345.
Lavalle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical report No. (pp. 98–11)
Lee, D., & Ott, C. (2011). Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Autonomous Robots, 31(2–3), 115–131.
Levine, S., & Koltun, V. (2012). Continuous inverse optimal control with locally optimal examples. In International Conference on Machine Learning.
Levy, B. (2006). Laplace-beltrami eigenfunctions towards an algorithm that “understands” geometry. In Shape Modeling International (pp. 13).
Lipman, Y., Sorkine, O., Levin, D.C.O.D., Rössl, C., & Seidel, H.P. (2004). Differential coordinates for interactive mesh editing. In Shape Modeling International (pp. 181–190).
Lipman, Y., Sorkine, O., Alexa, M., Cohen-Or, D., Levin, D., Rössl, C., et al. (2005). Laplacian framework for interactive mesh editing. International Journal of Shape Modeling, 11(1), 43–62.
Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395–416.
Meyer, M., Desbrun, M., Schröder, P., & Barr, A. H. (2002). Visualization and mathematics (pp. 35–57)., Discrete differential-geometry operators for triangulated 2-manifolds Berlin: Springer.
Mombaur, K., Olivier, A. H., & Crétual, A. (2013). Modeling, simulation and optimization (Vol. 18, pp. 165–179)., Forward and inverse optimal control of bipedal running Berlin: Springer.
Nakamura, Y. (1991). Advanced robotics—redundancy and optimization. Reading, MA: Addison-Wesley Publishing.
Nierhoff, T., & Hirche, S. (2012). Fast trajectory replanning using laplacian mesh optimization. In IEEE International Conference on Control, Automation, Robotics and Vision.
Nierhoff, T. (2013a). http://www.itr.ei.tum.de/fileadmin/w00bok/www/CodeExamples/laplacianHardConstraints.m. Accessed 24 June 2015.
Nierhoff, T. (2013b). http://www.itr.ei.tum.de/fileadmin/w00bok/www/CodeExamples/laplacianSoftConstraints.m. Accessed 24 June 2015.
Nierhoff, T., Hirche, S., & Nakamura, Y. (2013). Multiresolution laplacian trajectory replanning. In Proceedings of the Annual Conference of RSJ.
Nierhoff, T., Hirche, S., & Nakamura, Y. (2014). Sampling-based trajectory imitation in constrained environments using laplacian-rrt*. In IEEE/RSJ International Conference on Intelligent Robots and Systems.
Nierhoff, T., Hirche, S., & Nakamura, Y. (2013) Variable positional constraints for laplacian trajectory editing. In DGR-Tage.
Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S. (2009). Learning and generalization of motor skills by learning from demonstration. In IEEE International Conference on Robotics and Automation (pp. 1293–1298).
Pham, Q.C. (2011). Fast trajectory correction for nonholonomic mobile robots using affine transformations. In Robotics: Science and Systems.
Pham, Q.C., & Nakamura, Y. (2013). A new trajectory deformation algorithm based on affine transformations. In International Joint Conference on Artificial Intelligence.
Quinlan, S., & Khatib, O. (1993). Elastic bands: Connecting path planning and control. In IEEE International Conference on Robotics and Automation (pp. 802–807).
Reuter, M., Biasotti, S., Giorgi, D., Patanè, G., & Spagnuolo, M. (2009). Discrete laplace-beltrami operators for shape analysis and segmentation. Computational Geometry, 33(3), 381–390.
Schaal, S. (2006). Dynamic movement primitives—A framework for motor control in humans and humanoid robotics. Adaptive Motion of Animals and Machines (pp. 261–280).
Schulman, J., Duan, Y., Ho, J., Lee, A., Awwal, I., Bradlow, H., et al. (2014). Motion planning with sequential convex optimization and convex collision checking. International Journal of Robotics Research, 33(9), 1251–1270.
Shoemake, K. (1985). Animating rotation with quaternion curves. ACM International Conference on Computer Graphics and Interactive Techniques, 19, 245–254.
Sorkine, O., & Alexa, M. (2007). As-rigid-as-possible surface modeling. In Eurographics Symposium on Geometry Processing (pp. 109–116).
Sorkine, O., & Cohen-Or, D. (2004). Least-squares meshes. In Shape Modeling International (pp. 191–199).
Strichartz, R. S. (1983). Analysis of the laplacian on the complete riemannian manifold. Journal of Functional Analysis, 52(1), 48–79.
Taubin, G. (1995). A signal processing approach to fair surface design. In ACM Special Interest Group on Graphics and Interactive Techniques (pp. 351–358).
Umeyama, S. (1991). Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4), 376–380.
Wardetzky, M., Mathur, S., Kälberer, F., & Grinspun, E. (2007). Discrete laplace operators: No free lunch. In Eurographics Symposium on Geometry Processing (pp. 33–37).
Yamane, K., & Nakamura, Y. (2003). Natural motion animation through constraining and deconstraining at will. IEEE Transactions on Visualization and Computer Graphics, 9, 352–360.
Zhang, H., van Kaick, O., & Dyer, R. (2010). Spectral mesh processing. Computer Graphics Forum, 29(6), 1865–1894.
Zhou, K., Huang, J., Snyder, J., Liu, X., Bao, H., Guo, B., et al. (2005). Large mesh deformation using the volumetric graph laplacian. ACM Transactions on Graphics, 24(3), 496–503.
Zucker, M., Ratliff, N., Dragan, A., Pivtoraiko, M., Klingensmith, M., Dellin, C., et al. (2013). Chomp: Covariant hamiltonian optimization for motion planning. International Journal of Robotics Research, 32, 1164–1193.
Acknowledgments
This work was partially supported by the EU Horizon2020 project RAMCIP, under Grant Agreement No. 643433 and by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (S), 2008-2012, 20220001, “Establishing Human-Machine Communication through Kinesiology and Linguistics Integration” (PI: Y. Nakamura).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1 (mp4 106809 KB)
Rights and permissions
About this article
Cite this article
Nierhoff, T., Hirche, S. & Nakamura, Y. Spatial adaption of robot trajectories based on laplacian trajectory editing. Auton Robot 40, 159–173 (2016). https://doi.org/10.1007/s10514-015-9442-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-015-9442-3