Skip to main content

Physics-Based Motion Planning: Evaluation Criteria and Benchmarking

  • Conference paper
  • First Online:
Robot 2015: Second Iberian Robotics Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 417))


Motion planning has evolved from coping with simply geometric problems to physics-based ones that incorporate the kinodynamic and the physical constraints imposed by the robot and the physical world. Therefore, the criteria for evaluating physics-based motion planners goes beyond the computational complexity (e.g. in terms of planning time) usually used as a measure for evaluating geometrical planners, in order to consider also the quality of the solution in terms of dynamical parameters. This study proposes an evaluation criteria and analyzes the performance of several kinodynamic planners, which are at the core of physics-based motion planning, using different scenarios with fixed and manipulatable objects. RRT, EST, KPIECE and SyCLoP are used for the benchmarking. The results show that KPIECE computes the time-optimal solution with heighest success rate, whereas, SyCLoP compute the most power-optimal solution among the planners used.

J. Rosell—This work was partially supported by the Spanish Government through the projects DPI2011-22471, DPI2013-40882-P and DPI2014-57757-R. Muhayyuddin is supported by the Generalitat de Catalunya through the grant FI-DGR 2014.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Tsianos, K.I., Sucan, I.A., Kavraki, L.E.: Sampling-based robot motion planning: Towards realistic applications. Computer Science Review 1(1), 2–11 (2007)

    Article  Google Scholar 

  2. Ladd, A.M., Kavraki, L.E.: Motion planning in the presence of drift, underactuation and discrete system changes. In: Robotics: Science and Systems, pp. 233–240 (2005)

    Google Scholar 

  3. Zickler, S., Veloso, M.M.: Variable level-of-detail motion planning in environments with poorly predictable bodies. In: Proc. of the European Conf. on Artificial Intelligence Montpellier, pp. 189–194 (2010)

    Google Scholar 

  4. Russell, S.: Open Dynamic Engine (2007).

  5. Reif, J.H.: Complexity of the move’s problem and generalizations. In: Proc. of the 20th Annual IEEE Conf. on Foundations of Computer Science, pp. 421–427 (1979)

    Google Scholar 

  6. Cheng, P., Pappas, G., Kumar, V.: Decidability of motion planning with differential constraints. In: Proc. IEEE Int. Conf. on Robotics and Automation, pp. 1826–1831 (2007)

    Google Scholar 

  7. Zickler, S., Veloso, M.: Efficient physics-based planning: sampling search via non-deterministic tactics and skills. In: Proc. of The 8th Int. Conf. on Autonomous Agents and Multiagent Systems, vol. 1, pp. 27–33 (2009)

    Google Scholar 

  8. Zickler, S., Veloso, M.: Playing creative soccer: randomized behavioral kinodynamic planning of robot tactics. In: RoboCup 2008: Robot Soccer World Cup XII, pp. 414–425. Springer (2009)

    Google Scholar 

  9. NVIDIA: Physx.

  10. Plaku, E.: Motion planning with discrete abstractions and physics-based game engines. In: Proc. of the Int. Conf. on Motion in Games, pp. 290–301. Springer (2012)

    Google Scholar 

  11. Plaku, E., Kavraki, L., Vardi, M.: Motion planning with dynamics by a synergistic combination of layers of planning. IEEE Tran. on Robotics 26(3), 469–482 (2010)

    Article  Google Scholar 

  12. Erwin, C.: Bullet physics library (2013).

  13. Muhayyudin, Akbari, A., Rosell, J.: Ontological physics-based motion planning for manipulation. In: Proc. of IEEE Int. Conf. on Emerging Technologies and Factory Automation (ETFA) (2015)

    Google Scholar 

  14. Sucan, I., Kavraki, L.E.: A sampling-based tree planner for systems with complex dynamics. IEEE Transactions on Robotics 28(1), 116–131 (2012)

    Article  Google Scholar 

  15. Akbari, A., Muhayyudin, Rosell, J.: Task and motion planning using physics-based reasoning. In: Proc. of the IEEE Int. Conf. on Emerging Technologies and Factory Automation (2015)

    Google Scholar 

  16. Akbari, A., Muhayyuddin, Rosell, J.: Reasoning-based evaluation of manipulation actions for efficient task planning. In: ROBOT2015: Second Iberian Robotics Conference. Springer (2015)

    Google Scholar 

  17. Lozano-Pérez, T.: Spatial Planning: A Configuration Space Approach. IEEE Trans. on Computers 32(2), 108–120 (1983)

    Article  MATH  Google Scholar 

  18. Kavraki, L., Svestka, P., Latombe, J.C., Overmars, M.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. on Robotics and Automation 12(4), 566–580 (1996)

    Article  Google Scholar 

  19. Lavalle, S.M., Kuffner, J.J.: Rapidly-exploring random trees: progress and prospects. In: Algorithmic and Computational Robotics: New Directions, pp. 293–308 (2001)

    Google Scholar 

  20. Donald, B., Xavier, P., Canny, J., Reif, J.: Kinodynamic motion planning. Journal of the ACM 40(5), 1048–1066 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  21. LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. The Int. Journal of Robotics Research 20(5), 378–400 (2001)

    Article  Google Scholar 

  22. Hsu, D., Latombe, J.C., Motwani, R.: Path planning in expansive configuration spaces. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, vol. 3, pp. 2719–2726. IEEE (1997)

    Google Scholar 

  23. Hsu, D., Kindel, R., Latombe, J.C., Rock, S.: Randomized kinodynamic motion planning with moving obstacles. The Int. Journal of Robotics Research 21(3), 233–255 (2002)

    Article  Google Scholar 

  24. Hogan, N.: Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans. on Automatic Control 29(8), 681–690 (1984)

    Article  MATH  Google Scholar 

  25. Rosell, J., Pérez, A., Aliakbar, A., Muhayyuddin, Palomo, L., García, N.: The kautham project: a teaching and research tool for robot motion planning. In: Proc. of the IEEE Int. Conf. on Emerging Technologies and Factory Automation (2014)

    Google Scholar 

  26. Sucan, I., Moll, M., Kavraki, L.E., et al.: The open motion planning library. IEEE Robotics & Automation Magazine 19(4), 72–82 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jan Rosell .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Gillani, M., Akbari, A., Rosell, J. (2016). Physics-Based Motion Planning: Evaluation Criteria and Benchmarking. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 417. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27145-3

  • Online ISBN: 978-3-319-27146-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics