Advertisement

Autonomous Robots

, 28:113 | Cite as

Elastic roadmaps—motion generation for autonomous mobile manipulation

  • Yuandong Yang
  • Oliver Brock
Article

Abstract

The autonomous execution of mobile manipulation tasks in unstructured, dynamic environments requires the consideration of various motion constraints. The task itself imposes constraints, of course, but so do the kinematic and dynamic limitations of the manipulator, unpredictably moving obstacles, and the global connectivity of the workspace. All of these constraints need to be updated continuously in response to sensor feedback. We present the elastic roadmap framework, a novel feedback motion planning approach capable of satisfying all of these motion constraints and their respective feedback requirements. This framework is validated in simulation and real-world experiments using a mobile manipulation platform and a stationary manipulator.

Keywords

Motion generation Mobile manipulation Planning and control Feedback planning Hybrid systems for motion 

Supplementary material

10514_2009_9151_MOESM1_ESM.m1v (11.7 mb)
Below is the link to the electronic supplementary material. (M1V ??? kB)

References

  1. Amato, N., Bayazit, B., Dale, L., Jones, C., & Vallejo, D. (1998). OBPRM: An obstacle-based PRM for 3D workspaces. In Robotics: The algorithmic perspective. Wellesley: AK Peters. Google Scholar
  2. Barraquand, J., & Latombe, J.-C. (1991). Robot motion planning: A distributed representation approach. International Journal of Robotics Research, 10(6), 628–649. CrossRefGoogle Scholar
  3. Brock, O., & Grupen, R. (2005). Final report for the NSF/NASA Workshop on Autonomous Mobile Manipulation (AMM), November 2005. http://rbo.cs.umass.edu/amm/results.html.
  4. Brock, O., & Kavraki, L. E. (2001). Decomposition-based motion planning: A framework for real-time motion planning in high-dimensional configuration spaces. In Proc. int. conf. on robotics and automation. Google Scholar
  5. Brock, O., & Khatib, O. (2002). Elastic strips: A framework for motion generation in human environments. International Journal of Robotics Research, 21(12), 1031–1052. CrossRefGoogle Scholar
  6. Burns, B., & Brock, O. (2005). Toward optimal configuration space sampling. In Proceedings of robotics: Science and systems (RSS). Google Scholar
  7. Burridge, R. R., Rizzi, A. A., & Koditschek, D. E. (1999). Sequential composition of dynamically dexterous robot behaviors. International Journal of Robotics Research, 18(6), 534–555. CrossRefGoogle Scholar
  8. Chang, K.-S., & Khatib, O. (2000). Operational space dynamics: Efficient algorithms for modelling and control of branching mechanisms. In Proc. int. conf. on robotics and automation (pp. 850–856). Google Scholar
  9. Chen, P. C., & Hwang, Y. K. (1998). SANDROS: A dynamic graph search algorithm for motion planning. IEEE Transactions on Robotics and Automation, 14(3), 390–403. CrossRefGoogle Scholar
  10. Choi, W., & Latombe, J.-C. (1991). A reactive architecture for planning and executing robot motions with incomplete knowledge. In Proc. int. conf. on intelligent robots and systems (Vol. 1, pp. 24–29). Google Scholar
  11. Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. E., & Thrun, S. (2005). Principles of robot motion. Cambridge: MIT Press. zbMATHGoogle Scholar
  12. Conner, D. C., Rizzi, A. A., & Choset, H. (2003). Composition of local potential functions for global robot control and navigation. In Proc. int. conf. on intelligent robots and systems (pp. 3546–3551). Google Scholar
  13. Conner, D., Choset, H., & Rizzi, A. (2006). Integrated planning and control for convex-bodied nonholonomic systems using local feedback control policies. In Proceedings of robotics: Science and systems, Philadelphia, USA, August 2006. Google Scholar
  14. Connolly, C. I., & Grupen, R. A. (1993). One the applications of harmonic functions to robotics. Journal of Robotic Systems, 10(7), 931–946. zbMATHCrossRefGoogle Scholar
  15. Cortés, J., Jaillet, L., & Siméon, T. (2008). Disassembly path planning for complex articulated objects. IEEE Transactions on Robotics. Google Scholar
  16. Diankov, R., Ratliff, N., Ferguson, D., Srinivasa, S., & Kuffner, J. (2008). Bispace planning: Concurrent multi-space exploration. In Proceedings of robotics: Science and systems (RSS), ETH Zurich, June 2008. Google Scholar
  17. Franklin, G. F., Powell, J. D., & Emami-Naeini, A. (1994). Feedback control of dynamic systems. Reading: Addison-Wesley. Google Scholar
  18. Garber, M., & Lin, M. C. (2002). Constraint-based motion planning using Voronoi diagrams. In Proc. of the workshop on the algorithmic foundations of robotics. Google Scholar
  19. Gayle, R., Klingler, K. R., & Xavier, P. G. (2007). Lazy reconfiguration forest (LRF): An approach for motion planning with mulitple tasks in dynamic environments. In Proc. int. conf. on robotics and automation. Google Scholar
  20. Hauser, K., Bretl, T., Harada, K., & Latombe, J.-C. (2006). Using motion primitives in probabilistic sample-based planning for humanoid robots. In Proc. of the workshop on the algorithmic foundations of robotics (pp. 507–522). Google Scholar
  21. Hsu, D., Kindel, R., Latombe, J.-C., & Rock, S. (2000). Randomized kinodynamic motion planning with moving obstacles. In Proc. of the workshop on the algorithmic foundations of robotics (pp. 247–264). Google Scholar
  22. Hsu, D., Latombe, J.-C., & Kurniawati, H. (2005a). On the probabilistic foundations of probabilistic roadmap planning. In Proceedings of the international symposium of robotics research. Google Scholar
  23. Hsu, D., Sánchez-Ante, G., & Sun, Z. (2005b). Hybrid prm sampling with a cost-sensitive adaptive strategy. In Proc. int. conf. on robotics and automation. Google Scholar
  24. Huber, M., & Grupen, R. A. (1997). A feedback control structure for on-line learning tasks. Robotics and Autonomous Systems, 22(3–4), 303–315. CrossRefGoogle Scholar
  25. Jaillet, L., & Siméon, T. (2004). A PRM-based motion planner for dynamically changing environments. In Proc. int. conf. on intelligent robots and systems. Google Scholar
  26. Jaillet, L., & Siméon, T. (2008). Path deformation roadmaps: compact graphs with useful cycles for motion planning. International Journal of Robotics Research. Google Scholar
  27. Kallmann, M., & Matarić, M. (2004). Motion planning using dynamic roadmaps. In Proc. int. conf. on robotics and automation (pp. 4399–4404). Google Scholar
  28. Kavraki, L. E., Švestka, 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(4), 566–580. CrossRefGoogle Scholar
  29. Khatib, O. (1987). A unified approach to motion and force control of robot manipulators: The operational space formulation. International Journal of Robotics and Automation, 3(1), 43–53. CrossRefGoogle Scholar
  30. Kuffner, J., & LaValle, S. M. (2000). RRT-connect: An efficient approach to single-query path planning. In Proc. int. conf. on robotics and automation (Vol. 2, pp. 995–1001). Google Scholar
  31. Kurniawati, H., & Hsu, D. (2004). Workspace importance sampling for probabilistic roadmap planning. In Proc. int. conf. on intelligent robots and systems (pp. 1618–1623). Google Scholar
  32. LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press. zbMATHGoogle Scholar
  33. Leven, P., & Hutchinson, S. (2002). A framework for real-time path planning in changing environments. International Journal of Robotics Research, 21(12), 999–1030. CrossRefGoogle Scholar
  34. Lindemann, S. R., Hussein, I. I., & LaValle, S. M. (2006). Real time feedback control for nonholonomic mobile robots with obstacles. In Proceedings of the IEEE conference on decision and control (pp. 2406–2411). San Diego, USA, December 2006. Google Scholar
  35. Morales, M., Tapia, L., Pearce, R., Rodriguez, S., & Amato, N. M. (2004). A machine learning approach for feature-sensitive motion planning. In Proc. of the workshop on the algorithmic foundations of robotics, Utrecht/Zeist, The Netherlands. Google Scholar
  36. Oriolo, G., & Mongillo, C. (2005). Motion planning for mobile manipulators along given ed-effector paths. In Proc. int. conf. on robotics and automation (pp. 2166–2172). Barcelona, Spain. Google Scholar
  37. Plaku, E., Kavraki, L. E., & Vardi, M. Y. (2007). Discrete search leading continuous exploration for kinodynamic motion planning. In Proceedings of robotics: Science and systems (RSS) (pp. 313–320). Atlanta, USA, June 2007. Google Scholar
  38. Quinlan, S., & Khatib, O. (1993). Elastic bands: Connecting path planning and control. In Proc. int. conf. on robotics and automation (Vol. 2, pp. 802–807). Atlanta, USA. Google Scholar
  39. Rimon, E., & Koditschek, D. E. (1992). Exact robot navigation using artificial potential fields. IEEE Transactions on Robotics and Automation, 8(5), 501–518. CrossRefGoogle Scholar
  40. Sato, K. (1987). Collision avoidance in multi-dimensional space using Laplace potential. In Proceedings of the 15th conference of the robotics society of Japan (pp. 155–156). Google Scholar
  41. Sentis, L., & Khatib, O. (2005). Synthesis of whole-body behaviors through hierarchical control of behavioral primitives. International Journal of Humanoid Robots, 2(4), 505–518. CrossRefGoogle Scholar
  42. Siméon, T., Laumond, J.-P., & Nissoux, C. (2000). Visibility-based probabilistic roadmaps for motion planning. Journal of Advanced Robotics, 14(6), 477–494. CrossRefGoogle Scholar
  43. Siméon, T., Laumonde, J.-P., Cortés, J., & Sahbani, A. (2004). Manipulation planning with probabilistic roadmaps. International Journal of Robotics Research, 23(7–8), 729–746. Google Scholar
  44. Stilman, M. (2007). Task constrained motion planning in robot joint space. In Proc. int. conf. on intelligent robots and systems, October 2007. Google Scholar
  45. Tang, X., Thomas, S., & Amato, N. M. (2005). Planning with reachable distances: Fast enforcement of closure constraints. In Proc. int. conf. on robotics and automation (pp. 2694–2699). Rome, Italy. Google Scholar
  46. van den Berg, J. P., & Overmars, M. H. (2004). Using workspace information as a guide to non-uniform sampling in probabilistic roadmap planners. In Proc. int. conf. on robotics and automation (pp. 453–460). Google Scholar
  47. van den Berg, J. P., & Overmars, M. H. (2005). Roadmap-based motion planning in dynamic environments. IEEE Transactions on Robotics and Automation, 21(5), 885–897. Google Scholar
  48. Vannoy, J., & Xiao, J. (2004). Real-time adaptive and trajectory-optimized manipulator motion planning. In Proc. int. conf. on intelligent robots and systems (Vol. 1, pp. 497–502). Google Scholar
  49. Yang, Y., & Brock, O. (2004). Adapting the sampling distribution in PRM planners based on an approximated medial axis. In Proc. int. conf. on robotics and automation (pp. 4405–4410). Google Scholar
  50. Yang, Y., & Brock, O. (2005). Efficient motion planning based on disassembly. In Proceedings of robotics: Science and systems (RSS). Google Scholar
  51. Yang, Y., & Brock, O. (2006). Elastic roadmaps: Globally task-consistent motion for autonomous mobile manipulation in dynamic environments. In Proceedings of robotics: Science and systems (RSS). Google Scholar
  52. Yang, L., & LaValle, S. M. (2003). The sampling-based neighborhood graph: A framework for planning and executing feedback motion strategies. In Proc. int. conf. on robotics and automation. Google Scholar
  53. Yao, Z., & Gupta, K. (2007). Path planning with general end-effector constraints. Robotics and Autonomous Systems, 55(4), 315–327. CrossRefGoogle Scholar
  54. Zucker, M., Kuffner, J., & Bagnell, J. (2008). Adaptive workspace biasing for sampling-based planners. In Proc. int. conf. on robotics and automation (pp. 3757–3762). Pasadena, CA, May 2008. Google Scholar
  55. Zucker, M., Kuffner, J., & Branicky, M. (2007). Multipartite RRTs for rapid replanning in dynamic environments. In Proc. int. conf. on robotics and automation (pp. 1603–1609). Roma, Italy, April 2007. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Robotics and Biology Laboratory, Department of Computers ScienceUniversity of Massachusetts AmherstAmherstUSA
  2. 2.Robotics and Biology Laboratory, School of Electrical Engineering and Computer ScienceTechnische Universität BerlinBerlinGermany

Personalised recommendations