Skip to main content
SpringerLink
Log in

Optimal path planning in cluttered environment using RRT*-AB

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

Rapidly exploring Random Tree Star (RRT*) has gained popularity due to its support for complex and high-dimensional problems. Its numerous applications in path planning have made it an active area of research. Although it ensures probabilistic completeness and asymptotic optimality, its slow convergence rate and large dense sampling space are proven problems. In this paper, an off-line planning algorithm based on RRT* named RRT*-adjustable bounds (RRT*-AB) is proposed to resolve these issues. The proposed approach rapidly targets the goal region with improved computational efficiency. Desired objectives are achieved through three novel strategies, i.e., connectivity region, goal-biased bounded sampling, and path optimization. Goal-biased bounded sampling is performed within boundary of connectivity region to find the initial path. Connectivity region is flexible enough to grow for complex environment. Once path is found, it is optimized gradually using node rejection and concentrated bounded sampling. Final path is further improved using global pruning to erode extra nodes. Robustness and efficiency of proposed algorithm is tested through experiments in different structured and unstructured environments cluttered with obstacles including narrow and complex maze cases. The proposed approach converges to shorter path with reduced time and memory requirements than conventional RRT* methods.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Lan X, Di Cairano S (2015) Continuous curvature path planning for autonomous vehicle maneuvers using RRT*. In: Paper presented at the European control conference (ECC)

  2. Zhang X, Chen J, Xin B (2014) Path planning for unmanned aerial vehicles in surveillance tasks under wind fields. J Cent South Univ 21(8):3079–3091. doi:10.1007/s11771-014-2279-7

    Article  Google Scholar 

  3. Lau G, Liu HHT (2013) Real-time path planning algorithm for autonomous border patrol: design, simulation, and experimentation. J Intell Robot Syst 75(3–4):517–539. doi:10.1007/s10846-013-9841-7

    Google Scholar 

  4. Ahmidi N, Hager GD, Ishii L, Gallia GL, Ishii M (2012) Robotic path planning for surgeon skill evaluation in minimally-invasive sinus surgery. In: Paper presented at international conference on medical image computing and computer-assisted intervention

  5. LaValle SM (2006) Planning algorithms. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  6. Elbanhawi M, Simic M (2014) Sampling-based robot motion planning: a review survey. IEEE Access 2:56–77

    Article  Google Scholar 

  7. Hwang YK (1992) Gross motion planning—a survey. ACM Comput Surv 24(3):219–291

    Article  Google Scholar 

  8. Goerzen C, Kong Z, Mettler B (2009) A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J Intell Robot Syst 57(1–4):65–100. doi:10.1007/s10846-009-9383-1

    MATH  Google Scholar 

  9. Hart PE (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107

    Article  MathSciNet  Google Scholar 

  10. Daniel K, Nash A, Koenig S (2010) Theta any-angle path planning on grids. J Artif Intell Res 39:533–579

    MathSciNet  MATH  Google Scholar 

  11. Wang X, Hou Z, Lv F, Tan M, Wang Y (2014) Mobile robots modular navigation controller using spiking neural networks. Neurocomputing 134:230–238. doi:10.1016/j.neucom.2013.07.055

    Article  Google Scholar 

  12. Zhu DQ, Li WC, Yan MZ, Yang SX (2014) The path planning of AUV based on D-S information fusion map building and bio-inspired neural network in unknown dynamic environment. Int J Adv Robot Syst. doi:10.5772/56346

    Google Scholar 

  13. Liang JH, Lee CH (2015) Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm. Adv Eng Softw 79:47–56. doi:10.1016/j.advengsoft.2014.09.006

    Article  Google Scholar 

  14. Arora T, Gigras Y, Arora V (2014) Robotic path planning using genetic algorithm in dynamic environment. Int J Comput Appl 89(11):8–12

    Google Scholar 

  15. Kala R (2012) Multi-robot path planning using co-evolutionary genetic programming. Expert Syst Appl 39(3):3817–3831. doi:10.1016/j.eswa.2011.09.090

    Article  Google Scholar 

  16. Zhu WR, Duan HB (2014) Chaotic predator-prey biogeography-based optimization approach for UCAV path planning. Aerosp Sci Technol 32(1):153–161. doi:10.1016/j.ast.2013.11.003

    Article  Google Scholar 

  17. Nosrati M, Karimi R, Hasanvand HA (2012) Investigation of the * (Star) search algorithms characteristics methods and approaches. World Appl Program 2(4):251–256

    Google Scholar 

  18. Aissa O, Xu H, Zhao G (2009) Survey and the relative issues on the path planning of mobile robot in rough terrain (Online)

  19. Noreen I, Khan A, Habib Z (2016) Optimal path planning using RRT* based approaches: a survey and future directions. Int J Adv Comput Sci Appl (IJACSA) 7:97–107

    Google Scholar 

  20. Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30(7):846–894. doi:10.1177/0278364911406761

    Article  MATH  Google Scholar 

  21. Karaman S, Walter M, Perez A, Frazzoli E, Teller S (2011) Anytime motion planning using the RRT. In: Paper presented at the IEEE international conference on robotics and automation (ICRA)

  22. Ju T, Liu S, Yang J, Sun D (2014) Rapidly exploring random tree algorithm-based path planning for robot-aided optical manipulation of biological cells. IEEE Trans Autom Sci Eng 11(3):649–657

    Article  Google Scholar 

  23. Yang K (2011) Anytime synchronized-biased-greedy rapidly-exploring random tree path planning in two dimensional complex environments. Int J Control Autom Syst 9(4):750–758. doi:10.1007/s12555-011-0417-7

    Article  Google Scholar 

  24. LaValle SM (1998) Rapidly-exploring random trees: a new tool for path planning

  25. Kavraki LE, Svestka P, Latombe J-C, Overmars M (1996) Probabilistic roadmaps for path planning in high dimensional configuration spaces. IEEE Trans Robot Autom 12(4):566–580

    Article  Google Scholar 

  26. Karaman S (2013) Sampling based optimal motion planning for non-holonomic dynamical systems. In: Paper presented at the IEEE international conference on robotics and automation (ICRA)

  27. Qureshi AH, Ayaz Y (2015) Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments. Robot Auton Syst 68:1–11. doi:10.1016/j.robot.2015.02.007

    Article  Google Scholar 

  28. Noreen I, Khan A, Habib Z (2016) A comparison of RRT, RRT* and RRT*-smart path planning algorithms. Int J Comput Sci Netw Secur 16:20–27

    Google Scholar 

  29. Loeve JW (2012) Finding time-optimal trajectories for the resonating arm using the RRT* algorithm. Delft University of Technology, Delft

    Google Scholar 

  30. Nasir J, Islam F, Malik U, Ayaz Y, Hasan O, Khan M, Saeed M (2013) RRT*-SMART: a rapid convergence implementation of RRT*. Int J Adv Robot Syst 10:1–12. doi:10.5772/56718

    Article  Google Scholar 

  31. Gammell JD, Srinivasa SS, Barfoot T D (2014) Informed RRT*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: Paper presented at the IEEE RSJ international conference on intelligent robots and systems (IROS), Chicago

  32. Robotics Datasets University of Freiberg. http://www2.informatik.uni-freiburg.de/~stachnis/datasets.html. Accessed 2016

  33. Choudhury S, Gammell JD, Barfoot TD, Srinivasa SS, Scherer S (2016) Regionally accelerated batch informed trees (rabit*): a framework to integrate local information into optimal path planning. In: Paper presented at the IEEE international conference on robotics and automation (ICRA), Stockholm, Sweden

  34. Svenstrup M, Bak T, Andersen HJ (2011) Minimising computational complexity of the RRT algorithm—a practical approach. In: Paper presented at the IEEE international conference on robotics and automation, Shanghai, China

Download references

Acknowledgements

This study is jointly supported by the research Grant of Higher Education Commission (HEC) of Pakistan (No. 20-2359/NRPU/R&D/HEC/12-6779) and by a Project of Korean Government (10073166).

Author information

Authors and Affiliations

  1. Department of Computer Sciences, COMSATS Institute of Information Technology, Lahore, Pakistan

    Iram Noreen, Amna Khan & Zulfiqar Habib

  2. Division of Advanced Mechanical Engineering, Kangwon National University, Chuncheon, Korea

    Hyejeong Ryu

  3. School of Electrical Engineering, Korea University, Seoul, Korea

    Nakju Lett Doh

Authors
  1. Iram Noreen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amna Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyejeong Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nakju Lett Doh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zulfiqar Habib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zulfiqar Habib.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noreen, I., Khan, A., Ryu, H. et al. Optimal path planning in cluttered environment using RRT*-AB. Intel Serv Robotics 11, 41–52 (2018). https://doi.org/10.1007/s11370-017-0236-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-017-0236-7

Keywords

Search

Navigation