Journal of Intelligent & Robotic Systems

, Volume 73, Issue 1–4, pp 763–782 | Cite as

Spline-Based RRT Path Planner for Non-Holonomic Robots

  • Kwangjin Yang
  • Sangwoo Moon
  • Seunghoon Yoo
  • Jaehyeon Kang
  • Nakju Lett Doh
  • Hong Bong Kim
  • Sanghyun Joo
Article

Abstract

Planning in a cluttered environment under differential constraints is a difficult problem because the planner must satisfy the external constraints that arise from obstacles in the environment and the internal constraints due to the kinematic/dynamic limitations of the robot. This paper proposes a novel Spline-based Rapidly-exploring Random Tree (SRRT) algorithm which treats both the external and internal constraints simultaneously and efficiently. The computationally expensive numerical integration of the system dynamics is replaced by an efficient spline curve parameterization. In addition, the SRRT guarantees continuity of curvature along the path satisfying any upper-bounded curvature constraints. This paper presents the underlying theory to the SRRT algorithm and presents simulation and experiment results of a mobile robot efficiently navigating through cluttered environments.

Keywords

Differential constraints Rapidly-exploring random tree Spline curve parameterization Mobile robot 

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References

  1. 1.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)CrossRefMATHGoogle Scholar
  2. 2.
    Jacobs, P., Canny J.: Planning smooth paths for mobile robots. In: IEEE Int. Conf. Robotics and Automation, Scottsdale (1989)Google Scholar
  3. 3.
    Fraichard, T.: Smooth trajectory planning for a car in a structured world. In: IEEE Int. Conf. Robotics and Automation, Sacramento (1991)Google Scholar
  4. 4.
    Anderson, E., Beard, R., McLain, T.: Real-time dynamic trajectory smoothing for unmanned air vehicles. IEEE Trans. Control. Syst. Technol. 13(3), 471–477 (2005)CrossRefGoogle Scholar
  5. 5.
    Fraichard, T., Scheuer, A.: From Reeds and Shepp’s to continuous-curvature paths. IEEE Trans. Robot. 20(6), 1025–1035 (2004)CrossRefGoogle Scholar
  6. 6.
    Kito, T., Ota, J., Katsuiu, R., Mizuta, T., Arai, T., Ueyama, T., Nishiyama, T.: Smooth path planning by using visibility graph-like method. In: IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003)Google Scholar
  7. 7.
    Connors, J., Elkaim, G.: Analysis of a spline based, obstacle avoiding path planning algorithm. In: IEEE 65th Vehicular Technology Conference (2007)Google Scholar
  8. 8.
    Howard, T., Green, C., Kelly, A., Ferguson, D.: State space sampling of feasible motions for high performance mobile robot navigation in complex environments. J. Field Robot. 25(6–7), 325–345 (2008)CrossRefGoogle Scholar
  9. 9.
    Thrun, S., Ragusa, C., Ray, D., et al.: Stanley: the robot that won the DARPA grand challenge. J. Field Robot. 23(9), 661–692 (2006)CrossRefGoogle Scholar
  10. 10.
    Urmson, C., et al.: Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot. 25(8), 425–466 (2008)CrossRefGoogle Scholar
  11. 11.
    Hundelshausen, F., et al.: Driving with tentacles: integral structures for sensing and motion. J. Field Robot. 25(9), 640–673 (2008)CrossRefGoogle Scholar
  12. 12.
    Scherer, S., Singh, S., Chamberlain, L., Elgersma, M.: Flying fast and low among obstacles: methodology and experiments. Int. J. Robot. Res. 27(5), 549–574 (2008)CrossRefGoogle Scholar
  13. 13.
    Likhachev, M., Ferguson, D.: Planning long dynamically feasible maneuvers for autonomous vehicles. Int. J. Robot. Res. 28(8), 933–945 (2009)CrossRefGoogle Scholar
  14. 14.
    Barraquand, J., Latombe, J.C.: Nonholonomic multibody mobile robots: controllability and motion planning in the presence of obstacles. Algorithmica 10(2–4), 121–155 (1993)CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Howard, T., Kelly, A.: Optimal rough terrain trajectory generation for wheeled mobile robots. Int. J. Robot. Res. 26(2), 141–166 (2007)CrossRefGoogle Scholar
  16. 16.
    LaValle, S.M., Kuffner, J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(50), 378–400 (2001)CrossRefGoogle Scholar
  17. 17.
    LaValle, S.M.: Rapidly-Exploring Random Trees: A New Tool for Path Planning. TR 98-11, Computer Science Dept., Iowa State University (1998)Google Scholar
  18. 18.
    Kuffner, J., LaValle, S.: RRT-connect: an efficient approach to single-query path planning. In: IEEE International Conference on Robotics and Automation, San Francisco (2000)Google Scholar
  19. 19.
    Yershova, A., Jaillet, L., Simeon, T., LaValle, S.: Dynamic-domain rrts: efficient exploration by controlling the sampling domain. In: IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005)Google Scholar
  20. 20.
    Burns, B., Brock, O.: Single query motion planning with utilityguided random trees. In: IEEE International Conference on Robotics and Automation, Rome, Italy (2007)Google Scholar
  21. 21.
    Fulgenzi, C., Tay, C., Spalanzani, A., Laugier C.: Probabilistic navigation in dynamic environment using rapidly-exploring random trees and Gaussian processes. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008)Google Scholar
  22. 22.
    Yang, K., Gan, S., Sukkarieh, S.: A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV. Adv. Robot. 27(6), 431–443 (2013)CrossRefGoogle Scholar
  23. 23.
    Aoude, G., Luders, B., Joseph, J., Roy, N., How, J.: Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns. Auton. Robot. 35(1), 51–76 (2013)CrossRefGoogle Scholar
  24. 24.
    Frazzoli, E., Dahleh, M., Feron, E.: Real-time motion planning for agile autonomous vehicles. J. Guid. Control. Dyn. 25(1), 116–129 (2002)CrossRefGoogle Scholar
  25. 25.
    Ferguson, D., Stentz, A.: Anytime RRTs. In: International Conference on Intelligent Robots and Systems. Beijing, China (2006)Google Scholar
  26. 26.
    Yang, K.: Anytime synchronized-biased-greedy rapidly-exploring random tree path planning in two dimensional complex environments. Int. J. Control. Autom. Syst. 9(4), 750–758 (2011)CrossRefGoogle Scholar
  27. 27.
    Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)CrossRefGoogle Scholar
  28. 28.
    Perez, A., Platt, R., Konidaris, G., Kaelbling, L., Lozano-Perez, T.: LQR-RRT*: optimal sampling-based motion planning with automatically derived extension heuristics. In: IEEE International Conference on Robotics and Automation, St. Paul (2012)Google Scholar
  29. 29.
    Jaillet, L., Porta, J.: Asymptotically-optimal path planning on manifolds. In: Robotics: Science and Systems, Sydney, Australia (2012)Google Scholar
  30. 30.
    Choudhury, S., Scherer, S., Singh, S.: RRT*-AR: sampling-based alternate routes planning with applications to autonomous emergency landing of a helicopter. In: IEEE International Conference on Robotics and Automation, Karlsruhe, Germany (2013)Google Scholar
  31. 31.
    Chakraborty, N., Akella, S., Trinkle, J.: Complementarity- based dynamic simulation for kinodynamic motion planning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA (2009)Google Scholar
  32. 32.
    Kanayama, Y., Hartman, B.: Smooth local-path planning for autonomous vehicles. Int. J. Robot. Res. 16(3), 263–283 (1997)CrossRefGoogle Scholar
  33. 33.
    Yang, K., Sukkarieh, S.: 3D smooth path planning for a UAV in cluttered natural environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008)Google Scholar
  34. 34.
    Yang, K., Sukkarieh, S.: An analytical continuous-curvature path-smoothing algorithm. IEEE Trans. Robot. 26(3), 561–568 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Kwangjin Yang
    • 1
  • Sangwoo Moon
    • 1
  • Seunghoon Yoo
    • 2
  • Jaehyeon Kang
    • 3
  • Nakju Lett Doh
    • 3
  • Hong Bong Kim
    • 4
  • Sanghyun Joo
    • 5
  1. 1.Department of Aerospace and Mechanical EngineeringKorea Air Force AcademyCheongwonRepublic of Korea
  2. 2.Department of Electrical Engineering and Computer ScienceKorea Air Force AcademyCheongwonRepublic of Korea
  3. 3.School of Electrical EngineeringKorea UniversitySeoulRepublic of Korea
  4. 4.Future Man ElectronicsDaejonRepublic of Korea
  5. 5.Agency for Defense DevelopmentDaejonRepublic of Korea

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