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Autonomous Robots

, Volume 32, Issue 4, pp 433–454 | Cite as

A dynamical system approach to realtime obstacle avoidance

  • Seyed Mohammad Khansari-Zadeh
  • Aude Billard
Article

Abstract

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM arm.

Keywords

Realtime obstacle avoidance Nonlinear dynamical system Harmonic potential function Robot manipulator 

Notes

Acknowledgements

This work was supported by the European Commission through the EU Project AMARSI (FP7-ICT-248311). The authors kindly thank E. Sauser for providing the RobotToolkit interface to control the Barrett WAM arm, and M. Duvanel for the vision system. The authors also thank M. Benallegue and A. Kheddar for providing the source code of the STP-BV method to generate bounding volumes from the point cloud of objects.

Supplementary material

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References

  1. Barbehenn, M., Chen, P. C., & Hutchinson, S. (1994). An efficient hybrid planner in changing environments. In IEEE int. conf. on robotics and automation (Vol. 4, pp. 2755–2760). Google Scholar
  2. Benallegue, M., Escande, A., Miossec, S., & Kheddar, A. (2009). Fast C1 proximity queries using support mapping of sphere-torus-patches bounding volumes. In Proc. IEEE int. conf. on robotics and automation (pp. 483–488). Google Scholar
  3. Borenstein, J., & Koren, Y. (1991). The vector field histogram—fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation, 7, 278–288. CrossRefGoogle Scholar
  4. Brock, O., & Khatib, O. (2002). Elastic strips: A framework for motion generation in human environments. The International Journal of Robotics Research, 21(12), 1031–1052. CrossRefGoogle Scholar
  5. Burns, B., & Brock, O. (2005). Toward optimal configuration space sampling. In Proc. of robotics: science and systems. Google Scholar
  6. Diankov, R., & Kuffner, J. (2007). Randomized statistical path planning. In Proc. of IEEE/RSJ int. conf. on robots and systems (IROS) (pp. 1–6). Google Scholar
  7. Feder, H. J. S., & Slotine, J.-J. E. (1997). Real-time path planning using harmonic potentials in dynamic environments. In Proc. of IEEE int. conf. on robotics and automation (ICRA) (Vol. 1, pp. 874–881). Google Scholar
  8. Fraichard, T., Hassoun, M., & Laugier, C. (1991). Reactive motion planning in a dynamic world. In Proc. of the IEEE int. conf. on advanced robotics (pp. 1028–1032). Google Scholar
  9. 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 Proc. of int. conf. on robotics and automation (pp. 2587–2592). Google Scholar
  10. Iossifidis, I., & Schöner, G. (2006). Dynamical systems approach for the autonomous avoidance of obstacles and joint-limits for a redundant robot arm. In Proc. of the IEEE int. conf. on intelligent robots and systems (IROS) (pp. 580–585). Google Scholar
  11. Kavraki, L. E., Svestka, 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
  12. Khansari-Zadeh, S. M., & Billard, A. (2011). Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Transactions on Robotics, 27(5), 943–957. ISSN 1552-3098. doi: 10.1109/TRO.2011.2159412. CrossRefGoogle Scholar
  13. Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5, 90–98. CrossRefGoogle Scholar
  14. Kim, J.-O., & Khosla, P. K. (1992). Real-time obstacle avoidance using harmonic potential functions. IEEE Transactions on Robotics and Automation, 8(3), 338–349. CrossRefGoogle Scholar
  15. Kuffner, J. J., & LaValle, S. M. (2000). RRT-connect: An efficient approach to single-query path planning. In Proc. of IEEE int. conf. on robotics and automation (Vol. 2, pp. 995–1001). Google Scholar
  16. Lahanas, M., Kemmerer, T., Milickovic, N., Karouzakis, K., Baltas, D., & Zamboglou, N. (2000). Optimized bounding boxes for three-dimensional treatment planning in brachytherapy. Medical Physics, 27(10), 2333–2342. CrossRefGoogle Scholar
  17. Lozano-Perez, T. (1983). Spatial planning: A configuration space approach. IEEE Transactions on Computers, 30, 108–120. MathSciNetCrossRefGoogle Scholar
  18. Lumelsky, V., & Skewis, T. (1990). Incorporating range sensing in the robot navigation function. IEEE Transactions on Systems, Man, and Cybernetics, 20, 1058–1069. CrossRefGoogle Scholar
  19. Maciejewski, A. A., & Klein, C. A. (1985). Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. The International Journal of Robotics Research, 4, 109–116. CrossRefGoogle Scholar
  20. Milne-Thomson, L. M. (1960). Theoretical hydrodynamics (4th edn.) London: Macmillan. zbMATHGoogle Scholar
  21. Park, D.-H., Hoffmann, H., Pastor, P., & Schaal, S. (2008). Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields. In Proc. of the IEEE int. conf. on humanoid robotics (pp. 91–98). Google Scholar
  22. Quinlan, S., & Khatib, O. (1993). Elastic bands: connecting path planning and control. In Proc. of the IEEE int. conf. on robotics and automation (ICRA) (Vol. 2, pp. 802–807). Google Scholar
  23. Shilane, Ph., Min, P., Kazhdan, M., & Funkhouser, Th. (2004). The Princeton shape benchmark. In Shape modeling international, Italy. Google Scholar
  24. Simmons, R. (1996). The curvature-velocity method for local obstacle avoidance. In Proc. of the IEEE int. conf. on robotics and automation (Vol. 4, pp. 3375–3382). Google Scholar
  25. Sprunk, Ch., Lau, B., Pfaffz, P., & Burgard, W. (2011). Online generation of kinodynamic trajectories for non-circular omnidirectional robots. In Proc. of IEEE int. conf. on robotics and automation (ICRA) (pp. 72–77). Google Scholar
  26. Toussaint, M. (2009). Robot trajectory optimization using approximate inference. In 25th int. conf. on machine learning (ICML) (pp. 1049–1056). Google Scholar
  27. Vannoy, J., & Xiao, J. (2008). Real-time adaptive motion planning (ramp) of mobile manipulators in dynamic environments with unforeseen changes. IEEE Transactions on Robotics, 24, 1199–1212. CrossRefGoogle Scholar
  28. Waydo, S., & Murray, R. M. (2003). Vehicle motion planning using stream functions. In Proc. of the IEEE int. conf. on the robotics and automation (ICRA) (Vol. 2, pp. 2484–2491). Google Scholar
  29. Welzl, E. (1991). Smallest enclosing disks (balls and ellipsoids). In H. Maurer (Ed.), New results and new trends in computer science (Vol. 555, pp. 359–370). Berlin/Heidelberg: Springer. CrossRefGoogle Scholar
  30. Yang, Y., & Brock, O. (2007). Elastic roadmaps: Globally task-consistent motion for autonomous mobile manipulation in dynamic environments. In Proc. robotics: science and systems. Google Scholar
  31. Yoshida, E., & Kanehiro, F. (2011). Reactive robot motion using path replanning and deformation. In Proc. IEEE int. conf. on robotics and automation (pp. 5457–5462). Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.LASA Laboratory, School of EngineeringEcole Polytechnique Federale de Lausanne (EPFL)LausanneSwitzerland

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