[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

DVG+A* and RRT Path-Planners: A Comparison in a Highly Dynamic Environment

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This work provides a deeper comparison between two path planning algorithms, the Dynamic Visibility Graph A Star (DVG+A*) and Rapidly–exploring Random Trees (RRT), when applied in a high dimension and dynamic environment, which is the RoboCup Small Size League. The algorithms were compared under two different perspectives. In the first analysis, the algorithms were evaluated according to its computational time, path length and path safety in a static environment. Afterwards, they were evaluated regarding the accumulated computational time, number of recalculated paths, total navigation time and number of collisions in a dynamic environment. The static environment results have shown that the DVG+A* has a better overall performance than RRT, except for the path safety, however, some ideas on how to improve this were discussed. In the dynamic environment the algorithms performed similarly and with a high number of collisions during the experiments. Thus, showing the importance of using an obstacle avoidance algorithm combined with the path planner. In conclusion, the results obtained showed that both algorithms aren’t suitable for highly dynamic and cluttered environments, however, due how sparse the obstacles are in the SSL, they can still be used with some care. Regarding static environments, the DVG+A* has shown the best results.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of Data and Material

The authors confirm that the data supporting the findings of this study are available within this article and in the following repository: https://bit.ly/34IM5qe. All images shown in the article were made by the authors and can also be found at the repository.

References

  1. Ari, M.: Elements of Robotics SpringerOpen. Cham, Switzerland (2018)

    Google Scholar 

  2. Ayawli, B.B.K., Mei, X., Shen, M., Appiah, A.Y., Kyeremeh, F.: Mobile robot path planning in dynamic environment using voronoi diagram and computation geometry technique. IEEE Access 7, 86026–86040 (2019)

    Article  Google Scholar 

  3. Blanco-Claraco, J.L.: C++ 11 header-only library for building kd-trees. https://github.com/jlblancoc/nanoflann, Accessed 12 Aug 2020 (2020)

  4. Bondy, J.A., Murty, U.S.R., et al: Graph theory with applications, vol. 290. Macmillan London (1976)

  5. Brandt, D: Comparison of A* and RRT-connect motion planning techniques for self-reconfiguration planning. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 892–897. IEEE (2006)

  6. Braun, J, Brito, T, Lima, J, Costa, P, Nakano, A: A comparison of A* and RRT* algorithms with dynamic and real time constraint scenarios for mobile robots, pp. 398–405, https://doi.org/10.5220/0008118803980405(2019)

  7. Bruce, J, Veloso, MM: Real-time randomized path planning for robot navigation. In: Robot soccer world cup, pp 288–295. Springer (2002)

  8. Bruce, J.R.: Real-time motion planning and safe navigation in dynamic multi-robot environments. Tech. rep., Carnegie-Mellon Univ Pittsburgh Pa School of Computer Science (2006)

  9. Du, W., Islam, F., Likhachev, M.: Multi-resolution A*. arXiv:200406684 (2020)

  10. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  11. Hoy, M., Matveev, A.S., Savkin, A.V.: Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey. Robotica 33(3), 463–497 (2015)

    Article  Google Scholar 

  12. Huang, H.P., Chung, S.Y.: Dynamic visibility graph for path planning. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), vol. 3, pp 2813–2818. IEEE (2004)

  13. Kaludjer, H., Brezak, M., Petrović, I.: A visibility graph based method for path planning in dynamic environments. In: 2011 Proceedings of the 34th International Convention MIPRO, pp. 717–721. IEEE (2011)

  14. Kuffner, J.J., LaValle, S.M.: RRT–Connect: An efficient approach to single-query path planning. In: Robotics and Automation, 2000. Proceedings. ICRA’00. IEEE International Conference on, vol. 2, pp 995–1001. IEEE (2000)

  15. LaValle, S.M.: Rapidly-Exploring Random Trees: A New Tool For Path Planning. Citeseer (1998)

  16. Lin, Y., Saripalli, S.: Sampling-based path planning for uav collision avoidance. IEEE Trans. Intell. Transp. Syst. 18(11), 3179–3192 (2017)

    Article  Google Scholar 

  17. MacDougall, M., Ellis, G., Hashemi, A., Jackson, E., Lip, O.C., Ivanov, N., Petrie, J., Tonks-Turcotte, K., Sousa, C., Lai, K., Xu, B., Li, Q., Goto, E., Deutsch, D., Lee, A.B.M.: 2018 Team Description Paper: UBC Thunderbots (2018)

  18. Nash, A., Koenig, S.: Any-angle path planning. AI Mag. 34(4), 85–107 (2013)

    Google Scholar 

  19. Noreen, I., Khan, A., Asghar, K., Habib, Z.: A path-planning performance comparison of RRT*-AB with MEA* in a 2-dimensional environment. Symmetry 11(7), 945 (2019)

    Article  Google Scholar 

  20. Ommer, N., Ryll, A., Geiger, M.: TIGERs Mannheim (2019)

  21. Poudeh, A.G., Mohammadi, H.B., Hosseinikia, A., Esmaeelpourfard, S., Adhami-Mirhossein, A.: MRL extended team description 2016. In: Proceedings of the 19th International RoboCup Symposium (2016)

  22. RoboCup, SSL: Rules of the small size league. https://ssl.robocup.org/rules/, Accessed 19 Jul 2019 (2011)

  23. Rodríguez S., Rojas, E, Pérez, K., López, J., Quintero, C., Calderón, J.: Fast path planning algorithm for the RoboCup Small Size League. In: Robot Soccer World Cup, pp. 407–418. Springer (2014)

  24. da Silva Costa, L., Tonidandel, F.: Comparison and analysis of the DVG+A* and rapidly-exploring random trees path-planners for the Robocup-Small size league. In: 2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE), pp 1–6. IEEE (2019)

  25. Šišlák, D., Volf, P., Pechoucek, M.: Accelerated A* trajectory planning: Grid-based path planning comparison. In: 19th International Conference on Automated Planning and Scheduling (ICAPS), pp. 19–23. Thessaloniki, Greece, Citeseer (2009)

  26. Urmson, C., Simmons, R.: Approaches for heuristically biasing RRT growth. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453), vol. 2, pp 1178–1183. IEEE (2003)

  27. Vemula, A, Muelling, K, Oh, J: Path planning in dynamic environments with adaptive dimensionality. arXiv:160506853 (2016)

  28. Wang, J., Chi, W., Li, C., Wang, C., Meng, M.Q.H.: Neural RRT*: Learning-based optimal path planning. IEEE Trans. Autom. Sci. Eng. (2020a)

  29. Wang, J., Meng, M.Q.H., Khatib, O.: EB-RRT: Optimal motion planning for mobile robots. IEEE Trans. Autom. Sci. Eng. (2020b)

  30. Wei, K., Ren, B.: A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors 18(2), 571 (2018)

    Article  Google Scholar 

  31. Welzl, E.: Constructing the visibility graph for n-line segments in O(nn) time. Inf. Process. Lett. 20(4), 167–171 (1985)

    Article  Google Scholar 

  32. Yang, K., Sukkarieh, S.: An analytical continuous-curvature path-smoothing algorithm. IEEE Trans. Robot. 26(3), 561–568 (2010)

    Article  Google Scholar 

  33. Yang, S., Baum, M: Extended Kalman filter for extended object tracking. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 4386–4390. IEEE (2017)

  34. Zickler, S., Bruce, J., Biswas, J., Licitra, M., Veloso, M.: CMDragons 2009 extended team description. In: Proc. 14th International RoboCup Symposium, Singapore (2010)

Download references

Funding

This work was supported by University Center of FEI and it is based on a scientific project on mobile robots under funding number PBIC133/17.

Author information

Authors and Affiliations

  1. Department of Control and Automation Engineering, University Center of FEI, São Bernardo do Campo, SP, Brazil

    Leonardo da Silva Costa

  2. Department of Computer Science, University Center of FEI, São Bernardo do Campo, SP, Brazil

    Flavio Tonidandel

Authors
  1. Leonardo da Silva Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Flavio Tonidandel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Leonardo da Silva Costa implemented the algorithms, designed and performed the experiments, analyzed the data and wrote the manuscript. Flavio Tonidandel provided critical feedback, supervised and shaped the project, research, analysis and manuscript.

Corresponding author

Correspondence to Leonardo da Silva Costa.

Ethics declarations

Conflict of Interests

The authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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 University Center of FEI.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

da Silva Costa, L., Tonidandel, F. DVG+A* and RRT Path-Planners: A Comparison in a Highly Dynamic Environment. J Intell Robot Syst 101, 58 (2021). https://doi.org/10.1007/s10846-021-01326-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-021-01326-0

Keywords

Search

Navigation