Intelligent Service Robotics

, Volume 12, Issue 1, pp 125–136 | Cite as

Application of hybrid fast marching method to determine the real-time path for the biped robot

  • Ravi Kumar MandavaEmail author
  • Mrudul Katla
  • Pandu R. Vundavilli
Original Research Paper


The research in path planning is very intense in the field of robotics. Researchers around the world are interested in developing various methods to establish the path between a start and goal points in an obstacle-cluttered environment. In the present manuscript, the authors have proposed a solution methodology using fast marching method hybridized with regression search (FMMHRS) to generate an optimal path between the start and goal points in real time. Initially, the path is developed by using a fast marching method (FMM), and later on a regression search algorithm is employed to optimize the path obtained using FMM algorithm. In this work, the efficiency of the developed path planner is tested in simulations. Further, an experimental work is carried out in real-world environment on a two-legged robot to ascertain the path, its length, travelling time and convergence speed of the said approach. It has been observed that the proposed method is found to be superior and efficient when compared with some of the approaches available in the literature.


Path planning FMMHRS algorithm Two-legged robot 



  1. 1.
    Chang YC, Yamamoto Y (2009) Path planning of wheeled mobile robot with simultaneous free space locating capability. Intell Serv Robot 2:9–22CrossRefGoogle Scholar
  2. 2.
    LaValle S (2011) Motion planning. Robotics Automation Magazine, IEEE 18(1):79–89CrossRefGoogle Scholar
  3. 3.
    Kuffner JJ, LaValle S (2000) RRT-connecting efficient approach to single-query path planning. In: IEEE international conference on robotics and automation, vol 2, pp 995–1001Google Scholar
  4. 4.
    Hart P, Nilsson N, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4:100–107CrossRefGoogle Scholar
  5. 5.
    Dijkstra EW (1959) A note on two problems in connection with graphs. Numer Math 1:269–271MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Stentz A (1995) The focused D* algorithm for real-time re-planning. In: International joint conference on artificial intelligenceGoogle Scholar
  7. 7.
    Kovács B, Szayer G, Tajti F, Burdelis M, Korondi P (2016) A novel potential field method for path planning of mobile robots by adapting animal motion attributes. Robot Auton Syst 82:24–34CrossRefGoogle Scholar
  8. 8.
    Melchior P, Orsoni B, Oustaloup A (2001) Weyl fractional potential in path planning. In: European control conference (ECC), pp 1758–1763Google Scholar
  9. 9.
    Oustaloup A, Orsoni B, Melchior P, Linares H (2003) Path planning by fractional differentiation. Robotica 21(1):59–69CrossRefGoogle Scholar
  10. 10.
    Melchior P, Metoui B, Najar S, Abdelkrim MN, Oustaloup A (2009) Robust path planning for mobile robot based on fractional attractive force. In: American control conference (ACC), pp 1424–1429Google Scholar
  11. 11.
    Kelly A (1995) An intelligent predictive control approach to the high-speed cross-country autonomous navigation problem, engineeringGoogle Scholar
  12. 12.
    Thrun S, Bucken A, Burgard W, Fox D, Frohlinghaus T, Hennig D, Hofmann T, Krell M, Schimdt T (1998) Map learning and high-speed navigation in RHINO. In: Kortenkamp D, Bonasso RP, Murphy R (eds) AI-based mobile robots. Case studies of successful robot systems. MIT Press, CambridgeGoogle Scholar
  13. 13.
    Brock O, Khatib O (1999) High-speed navigation using the global dynamic window approach. In: IEEE international conference on robotics and automation, vol 1, pp 341–346Google Scholar
  14. 14.
    Philippsen R, Siegwart R (2003) Smooth and efficient obstacle avoidance for a tour guide robot. In: IEEE international conference on robotics and automation, vol 1, pp 446–451Google Scholar
  15. 15.
    Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J, Fong P, Gale J, Halpenny M, Hoffmann G, Lau K, Oakley C, Palatucci M, Pratt V, Stang P, Strohband S, Dupont C, Jendrossek L-E, Koelen C, Markey C, Rummel C, van Niekerk J, Jensen E, Alessandrini P, Bradski G, Davies B, Ettinger S, Kaehler A, Nefian A, Mahoney P (2006) Stanley: the robot that won the DARPA grand challenge: research articles. J Robot Syst 23(9):661–692Google Scholar
  16. 16.
    Howard TM, Kelly A (2007) Optimal rough terrain trajectory generation for wheeled mobile robots. Int J Robot Res 26(2):141–166CrossRefGoogle Scholar
  17. 17.
    Urmson C, Ragusa C, Ray D, Anhalt J, Bartz D, Galatali T, Gutierrez E, Johnston J, Clark M, Koon P, Mosher A, Struble J (2006) A robust approach to high-speed navigation for unrehearsed desert terrain. J Field Robot 23:467–508CrossRefzbMATHGoogle Scholar
  18. 18.
    Braid D, Broggi A, Schmiedel G (2006) The TerraMax autonomous vehicle. J Field Robot 23(9):693–708CrossRefGoogle Scholar
  19. 19.
    Vascak J, Rutrich M (2008) Path planning in dynamic environment using fuzzy cognitive maps. In: 2008 6th international symposium on applied machine intelligence and informatics. IEEE, pp 5–9Google Scholar
  20. 20.
    Li G, Tamura Y, Yamashita A, Asama H (2013) Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning. Int J Mechatron Autom 3(3):141–170CrossRefGoogle Scholar
  21. 21.
    Mandava RK, Bondada S, Vundavilli PR (2017) An optimized path planning for the mobile robot using potential field method and PSO algorithm. In: 7th international conference on soft computing and problem solving (socpros-2017), Bhubaneswar, IndiaGoogle Scholar
  22. 22.
    Sethian JA (1996) A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci 93(4):1591–1595MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Garrido Santiago, Moreno Luis, Blanco Dolores, Jurewicz Piotr (2011) Path planning for mobile robot navigation using voronoi diagram and fast marching. Int J Robot Aut (IJRA) 2(1):42–64Google Scholar
  24. 24.
    Melchior P, Orsoni B, Lavialle O, Poty A, Oustaloup A (2003) Consideration of obstacle danger level in path planning using A* and fast-marching optimization: comparative study. Signal Process 11:2387–2396CrossRefzbMATHGoogle Scholar
  25. 25.
    Chiang CH, Chiang PJ (2007) A comparative study of implementing fast marching method and A* search for mobile robot path planning in grid environment: effect of map resolution. In: Proceedings of IEEE advanced robotics and its social impacts, pp 1–6Google Scholar
  26. 26.
    Do QuocHuy, Mita Seiichi, Yoneda Keisuke (2013) A practical and optimal path planning for autonomous parking using fast marching algorithm and support vector machine. IEICE Trans Inf Syst 96(12):2795–2804CrossRefGoogle Scholar
  27. 27.
    Do QH, Mita S, Yoneda K (2014) Narrow passage path planning using fast marching method and support vector machine. In: 2014 IEEE intelligent vehicles symposium (IV)Google Scholar
  28. 28.
    Liu Y, Song R, Bucknall R (2015) A practical path planning and navigation algorithm for an unmanned surface vehicle using the fast marching algorithm. In: OCEANS 2015—Genova, Italy, pp 1–7Google Scholar
  29. 29.
    Tsitsiklis JN (1995) Efficient algorithms for globally optimal trajectories. IEEE Trans Autom Control 40(9):1528–1538MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Gomez JV (2012) Advanced applications of the fast marching square planning method. Master’s thesis, Carlos III UniversityGoogle Scholar
  31. 31.
    Forcadel N, Le Guyader C, Gout C (2008) Generalized fast marching method: applications to image segmentation. Numer Algorithms 48(1–3):189–211MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Basu S, Racoceanu D (2014) Reconstructing neuronal morphology from microscopy stacks using fast marching. In: IEEE international conference on image processing, pp 3597–3601Google Scholar
  33. 33.
    Qu X, Liu S, Wang F (2014) A new ray tracing technique for cross hole radar travel time tomography based on multistencils fast marching method and the steepest descend method. In: 15th international conference on ground penetrating radar, pp 503–508Google Scholar
  34. 34.
    Zhang X, Bording R (2011) Fast marching method seismic travel times with reconfigurable field programmable gate arrays. Can J Explor Geophys 36(1):60–68Google Scholar
  35. 35.
    Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79(1):12–49MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948Google Scholar
  37. 37.
    Mandava RK, Vundavilli PR (2018) Whole body motion generation of 18-DOF biped robot on flat surface during SSP and DSP. Int J Model Identif Control 29(3):266–277CrossRefGoogle Scholar
  38. 38.
    Kuffner Jr. JJ, Nishiwaki K, Kagami S, Inaba M, Inoue H (2001) Footstep planning among obstacles for biped robots. In: Proceedings of 2001 IEEE RSJ international conference on intelligent robots and systems, Maui, Hawaii, USA, Oct 29–NOV. 03, pp 500–505Google Scholar
  39. 39.
    Hildebrandt AC, Klischat M, Wahrmann D, Wittmann R, Sygulla F, Seiwald P, Rixen D, Buschmann T (2017) Real-time path planning in unknown environments for bipedal robots. IEEE Robot Autom Lett 2(4):1856–1863CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical SciencesIIT BhubaneswarBhubaneswarIndia

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