Skip to main content
SpringerLink
Log in

Stack-RRT*: A Random Tree Expansion Algorithm for Smooth Path Planning

  • Regular Papers
  • Robot and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

Most RRT-based extension algorithms can generate safe and smooth paths by combining parameter curve-based smoothing schemes. For example, the Spline-based Rapidly-exploring Random Tree (SRRT) guarantees that the generated paths are G2-continuous by considering a Bezier curve-based smoothing scheme. In this paper, we propose Stack-RRT*, a random tree expansion method that can be combined with different parameter curve-based smoothing schemes to produce feasible paths with different continuities for non-holonomic robots. Stack-RRT* expands the search for possible parent vertices by considering not only the set of vertices contained in the tree, as in the RRT-based algorithm, but also some newly created nodes close to obstacles, resulting in a shorter initial path than other RRT-based algorithms. In addition, the Stack-RRT* algorithm can achieve convergence by locally optimizing the connection relation of random tree vertices after each expansion. Rigorous simulations and analysis demonstrate that this new approach outperforms several existing extension schemes, especially in terms of the length of the planned paths.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. M. LaValle and J. J. Kuffner, “Rapidly-exploring random trees: Progress and prospects,” Algorithmic and computational robotics: new directions, no. 5, pp. 293–308, 2001.

  2. S. M. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” 1998.

  3. S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The International Journal of Robotics Research, vol. 30, no. 7, pp. 846–894, June 2011.

    Article  MATH  Google Scholar 

  4. I.-B. Jeong, S.-J. Lee, and J.-H. Kim, “Rrt*-quick: A motion planning algorithm with faster convergence rate,” Robot Intelligence Technology and Applications, pp. 67–76, 2015.

  5. I.-B. Jeong, S.-J. Lee, and J.-H. Kim, “Quick-rrt*: Triangular inequality-based implementation of rrt* with improved initial solution and convergence rate,” Expert Systems with Applications, vol. 123, pp. 82–90, June 2019.

    Article  Google Scholar 

  6. Y. Li, W. Wei, Y. Gao, D. Wang, and Z. Fan, “Pq-rrt*: An improved path planning algorithm for mobile robots,” Expert Systems with Applications, vol. 152, 113425, August 2020.

    Article  Google Scholar 

  7. F. Islam, J. Nasir, U. Malik, Y. Ayaz, and O. Hasan, “Rrt*-smart: Rapid convergence implementation of rrt towards optimal solution,” Proc. of IEEE International Conference on Mechatronics and Automation, pp. 1651–1656, August 2012.

  8. J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot, “Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2997–3004, November 2014.

  9. S. M. LaValle, Planning Algorithms, Cambridge University Press, 2006.

  10. J. Wang, W. Chi, C. Li, C. Wang, and M. Q.-H. Meng, “Neural rrt*: Learning-based optimal path planning,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 4, pp. 1748–1758, March 2020.

    Article  Google Scholar 

  11. X.-N. Bui, J.-D. Boissonnat, P. Soueres, and J.-P. Laumond, “Shortest path synthesis for dubins non-holonomic robot,” Proc. of the IEEE International Conference on Robotics and Automation, pp. 2–7, August 1994.

  12. E. P. Anderson, R. W. Beard, and T. W. McLain, “Real-time dynamic trajectory smoothing for unmanned air vehicles,” IEEE Transactions on Control Systems Technology, vol. 13, no. 3, pp. 471–477, April 2005.

    Article  Google Scholar 

  13. K. Mittal, J. Song, S. Gupta, and T. A. Wettergren, “Rapid path planning for dubins vehicles under environmental currents,” Robotics and Autonomous Systems, vol. 134, 103646, December 2020.

    Article  Google Scholar 

  14. T. Fraichard and A. Scheuer, “From reeds and shepp’s to continuous-curvature paths,” IEEE Transactions on Robotics, vol. 20, no. 6, pp. 1025–1035, November 2004.

    Article  Google Scholar 

  15. E. D. Lambert, R. Romano, and D. Watling, “Optimal smooth paths based on clothoids for car-like vehicles in the presence of obstacles,” International Journal of Control, Automation, and Systems, vol. 19, no. 3, pp. 1–20, September 2021.

    Google Scholar 

  16. K. Yang, S. K. Gan, and S. Sukkarieh, “An efficient path planning and control algorithm for ruav’s in unknown and cluttered environments,” Proc. of the 2nd International Symposium on UAVs, Reno, Nevada, USA, pp. 101–122, June 2009.

  17. K. Yang, S. Moon, S. Yoo, J. Kang, N. L. Doh, H. B. Kim, and S. Joo, “Spline-based rrt path planner for non-holonomic robots,” Journal of Intelligent & Robotic Systems, vol. 73, no. 1–4, pp. 763–782, October 2013.

    Google Scholar 

  18. K. Yang and S. Sukkarieh, “3d smooth path planning for a uav in cluttered natural environments,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 794–800, October 2008.

  19. M. Elbanhawi, M. Simic, and R. N. Jazar, “Continuous-curvature bounded trajectory planning using parametric splines.,” IDT/IIMSS/STET, vol. 262, pp. 513–522, June 2014.

    Google Scholar 

  20. M. Elbanhawi and M. Simic, “Randomised kinodynamic motion planning for an autonomous vehicle in semi-structured agricultural areas,” Biosystems Engineering, vol. 126, pp. 30–44, October 2014.

    Article  Google Scholar 

  21. M. Elbanhawi, M. Simic, and R. N. Jazar, “Continuous path smoothing for car-like robots using b-spline curves,” Journal of Intelligent & Robotic Systems, vol. 80, no. 1, pp. 23–56, 2015.

    Article  Google Scholar 

  22. E. Koyuncu and G. Inalhan, “A probabilistic b-spline motion planning algorithm for unmanned helicopters flying in dense 3d environments,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 815–821, October 2008.

  23. J. Krishnan, U. Rajeev, J. Jayabalan, and D. Sheela, “Optimal motion planning based on path length minimisation,” Robotics and Autonomous Systems, vol. 94, pp. 245–263, August 2017.

    Article  Google Scholar 

  24. M. Elbanhawi and M. Simic, “Sampling-based robot motion planning: A review,” IEEE Access, vol. 2, pp. 56–77, January 2014.

    Article  Google Scholar 

  25. Y. Liu and Y. Jiang, “Robotic path planning based on a triangular mesh map,” International Journal of Control, Automation, and Systems, vol. 18, pp. 2658–2666, 2020.

    Article  Google Scholar 

  26. K. Yang, “Anytime synchronized-biased-greedy rapidly-exploring random tree path planning in two dimensional complex environments,” International Journal of Control, Automation, and Systems, vol. 9, no. 4, pp. 750–758, 2011.

    Article  Google Scholar 

  27. M. Elbanhawi, M. Simic, and R. Jazar, “Randomized bidirectional b-spline parameterization motion planning,” IEEE Transactions on intelligent transportation systems, vol. 17, no. 2, pp. 406–419, September 2015.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China

    Bin Liao, Yi Hua, Fangyi Wan, Shenrui Zhu, Yipeng Zong & Xinlin Qing

Authors
  1. Bin Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fangyi Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shenrui Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yipeng Zong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xinlin Qing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fangyi Wan.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by the National Natural Science Foundation of China [grant number. 11972314].

Bin Liao received his B.S. degree from Nanchang Hangkong University, China, in 2017 and an M.S. degree from Northwestern Polytechnical University (NWPU) in 2020. He is pursuing a Ph.D. degree in NWPU. His current research interests include motion planning and multi-robot control.

Yi Hua received his B.S. degree from Chongqing University of Arts and Sciences in 2017 and the M.S. degree from Southwest University in 2020. His research interests include machine learning, network security, and signal processing.

Fangyi Wan received his B.S. degree in water resources and hydroelectric engineering and an M.S. degree in printing and packaging engineering from the Xi’an University of Technology, Xi’an, China, in 1994 and 1996, respectively, and a Ph.D. degree in mechanics engineering from Xi’an Jiaotong University, Xi’an, in 2003. He joined the School of Aeronautics, Northwestern Polytechnical University, Xi’an, as a Faculty Member, where he is currently an Associate Professor. His main research interests include vibration analysis and control, design, modeling, test, and health management of aircraft structures.

Shenrui Zhu received his B.S. degree from Northwestern Polytechnical University (NWPU), China, in 2019. Currently, he is pursuing an M.Eng. degree in NWPU. His research interests include artificial intelligence, prognostics, and health management.

Yipeng Zong received his B.S. degree from Northwestern Polytechnical University (NWPU), China, in 2019. Currently, he is pursuing an M.Eng. degree in NWPU. His research interests include fault diagnosis and prognosis.

Xinlin Qing received his M.Sc. degree from Tianjin University, Tianjin, China, in 1991, and a Ph.D. degree from Tsinghua University, Beijing, China, in 1993. He is currently a distinguished Professor at Northwestern Polytechnical University, Xian, China. His main research interests include structural health monitoring and advanced sensing technology.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, B., Hua, Y., Wan, F. et al. Stack-RRT*: A Random Tree Expansion Algorithm for Smooth Path Planning. Int. J. Control Autom. Syst. 21, 993–1004 (2023). https://doi.org/10.1007/s12555-021-0440-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-0440-2

Keywords

Search

Navigation