Using Gaussian process regression for efficient motion planning in environments with deformable objects

AAAI Workshop - Technical Report

Volumn WS-11-09, Issue , 2011, Pages 2-7

  Frank, Barbara ^a   Stachniss, Cyrill ^a   Abdo, Nichola ^a   Burgard, Wolfram ^a  

^a FRAUNHOFER IAIS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION TIME; DEFORMABLE OBJECT; DEFORMATION SIMULATION; GAUSSIAN PROCESS REGRESSION; INDIVIDUAL OBJECTS; MOBILE MANIPULATION; PHYSICAL DEFORMATION; PHYSICAL SIMULATION; PRE-PROCESSING STEP; PROBABILISTIC ROADMAP PLANNING; RUNTIMES; SIMULATION SYSTEMS; TRAINING DATA; TRAINING EXAMPLE;

COST BENEFIT ANALYSIS; DEFORMATION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); MOTION PLANNING; ROBOTS;

ROBOT PROGRAMMING;

EID: 80055029629     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Anshelevich, E.; Owens, S.; Lamiraux, F.; and Kavraki, L. 2000. Deformable volumes in path planning applications. In Proc. of the Int. Conf. on Robotics & Automation (ICRA).
2. Bayazit, O.; Lien, J.-M.; and Amato, N. 2002. Probabilistic roadmap motion planning for deformable objects. In Proc. of the Int. Conf. on Robotics & Automation (ICRA).
3. Frank, B.; Becker, M.; Stachniss, C.; Teschner, M.; and Burgard, W. 2008. Efficient path planning for mobile robots in environments with deformable objects. In Proc. of the Int. Conf. on Robotics & Automation (ICRA).
4. Frank, B.; Stachniss, C.; Schmedding, R.; Teschner, M.; and Burgard, W. 2009. Real-world robot navigation amongst deformable obstacles. In Proc. of the Int. Conf. on Robotics & Automation (ICRA).
5. Frank, B.; Schmedding, R.; Stachniss, C.; Teschner, M.; and Burgard, W. 2010. Learning the elasticity parameters of deformable objects with a manipulation robot. In Proc. of the Int. Conf. on Intelligent Robots and Systems (IROS).
6. Gayle, R.; Segars, P.; Lin, M.; and Manocha, D. 2005. Path planning for deformable robots in complex environments. In Proc. of Robotics: Science and Systems (RSS).
7. Hauth, M., and Strasser, W. 2004. Corotational Simulation of Deformable Solids. In Int. Conf. on Computer Graphics, Visualization, and Computer Vision (WSCG), 137-145.
8. Heidelberger, B.; Teschner, M.; Spillmann, J.; Mueller, M.; Gissler, M.; and Becker, M. 2006. DefColStudio 1.1.0. http://cg.informatik.uni-freiburg.de/ software.htm.
9. Holleman, C.; Kavraki, L.; and Warren, J. 1998. Planning paths for a flexible surface patch. In Proc. of the Int. Conf. on Robotics & Automation (ICRA).
10. Kavraki, L.; Svestka, P.; Latombe, J.-C.; and Overmars, M. 1996. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation 12(4):566-580. (Pubitemid 126774162)
11. Ko, J., and Fox., D. 2009. GP-BayesFilters: Bayesian filtering using gaussian process prediction and observation models. Autonomous Robots.
12. Maitin-Shepard, J.; M. Cusumano-Towner, J. L.; and Abbeel, P. 2010. Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding. In Proc. of the Int. Conf. on Robotics & Automation (ICRA).
13. Maris, B.; Botturi, D.; and Fiorini, P. 2010. Trajectory planning with task constraints in densely filled environments. In Proc. of the Int. Conf. on Intelligent Robots and Systems (IROS).
14. Mueller, M., and Gross, M. 2004. Interactive Virtual Materials. In Graphics Interface, 239-246.
15. Rasmussen, C. E., and Williams, C. K. 2006. Gaussian Processes for Machine Learning. The MIT Press.
16. Rodríguez, S.; Lien, J.-M.; and Amato, N. 2006. Planning motion in completely deformable environments. In Proc. of the Int. Conf. on Robotics & Automation (ICRA).
17. Vasudevan, S.; Ramos, F.; Nettleton, E.; and Durrant-Whyte, H. 2009. Gaussian process modeling of large scale terrain. Journal of Field Robotics 26(10).