Artificial Intelligence Review

, Volume 33, Issue 4, pp 307–327 | Cite as

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

  • Rahul KalaEmail author
  • Anupam Shukla
  • Ritu Tiwari


Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.


Path planning Robotics Fuzzy inference system A* Algorithm Genetic algorithm Heuristics Probabilistic fitness Hierarchical algorithms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alvarez A, Caiti A, Onken R (2004) Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J Ocean Eng 29(2): 418–429CrossRefGoogle Scholar
  2. Bohlin R, Kavraki LE (2000) Path planning using lazy PRM, Proceedings. IEEE Int Robot Autom ICRA ‘00 1: 521–528, San Francisco, CA, USAGoogle Scholar
  3. Carpin S, Pagello E (2009) An experimental study of distributed robot coordination. Robot Autonom Syst 57(2): 129–133CrossRefGoogle Scholar
  4. Castejon C, Blanco D, Boadai BL, Moreno L. et al (2005) Voronoi-based outdoor traversable region modelling. In: Patnaik S. (eds) Innovations in robot mobility and control. Springer-Verlag, Berlin, Heidelberg, pp 201–250Google Scholar
  5. Chen LH, Chiang CH (2003) New approach to intelligent control systems with self-exploring process. IEEE Trans Syst Man Cyber Part B Cyber 33(1): 56–66CrossRefGoogle Scholar
  6. Cortes J, Jaillet L, Simeon T (2008) Disassembly path planning for complex articulated objects. IEEE Trans Robot 24(2): 475–481CrossRefGoogle Scholar
  7. Ge SS, Lewis FL (2006) Autonomous mobile robot. Taylor and Francis, USAGoogle Scholar
  8. Goel AK (1994) Multistrategy adaptive path planning. IEEE Special Feature, pp 57–65Google Scholar
  9. Hazon N, Kaminka GA (2008) On redundancy, efficiency, and robustness in coverage for multiple robots. Towards Autonom Robot Syst 56(12): 1102–1114CrossRefGoogle Scholar
  10. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  11. Hui NB, Pratihar DK (2009) A comparative study on some navigation schemes of a real robot tackling moving obstacles. Robot Comp Integ Manufac doi: 10.1016/j.rcim.2008.12.003
  12. Hwang JY, Kim JS, Lim SS, Park KH (2003) A fast path planning by path graph optimization. IEEE trans Syst Man Cyber Part A Syst Humans 33(1): 121–128CrossRefGoogle Scholar
  13. Jan GE, Chang KY, Parberry I (2008) Optimal path planning for mobile robot navigation. IEEE/ASME Trans Mechat 13(4): 451–460CrossRefGoogle Scholar
  14. Jolly KG, Kumar RS, Vijayakumar R (2009) A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Robot Autonom Syst 57(1): 23–33CrossRefGoogle Scholar
  15. Juidette H, Youlal H (2000) Fuzzy dynamic path planning using genetic algorithms. IEEE Electron Lett 36(4): 374–376CrossRefGoogle Scholar
  16. Kala R et al (2009) Mobile robot navigation control in moving obstacle environment using genetic algorithm, artificial neural networks and A* algorithm. Proceedings of the IEEE world congress on computer science and information engineering (CSIE 2009). Los Angeles/Anaheim, USAGoogle Scholar
  17. Kambhampati S, Davis LS (1986) Multiresolution path planning for mobile robots. IEEE J Robot Autom RA-2(3): 135–145Google Scholar
  18. Kavraki LE, Svestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensionalconfiguration spaces. IEEE Trans Robot Autom 12(4): 566–580CrossRefGoogle Scholar
  19. Lai XC, Ge SS, Mamun AA (2007) Hierarchical incremental path planning and situation-dependent optimized dynamic motion planning considering accelerations. IEEE Trans Syst Man Cyber Part B Cyber 37(6): 1541–1554CrossRefGoogle Scholar
  20. Lee CH, Chiu MH (2009) Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm. Expert Syst Appl doi: 10.1016/j.eswa.2008.11.051
  21. Lima PU, Custodio LM et al (2005) Multi-robot systems. In: Patnaik S (eds) Innovations in robot mobility and control. Springer-Verlag, Berlin Heidelberg, pp 1–64Google Scholar
  22. Lin HS, Xiao J, Michalewicz Z (1994) Evolutionary algorithm for path planning in mobile robot environment. Proceedings of the first IEEE conference on evolutionary computation (ICEC’94). pp 211–216Google Scholar
  23. Lozano PT, Wesley MA (1979) An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, pp 560–570Google Scholar
  24. Ng KC, Trivedi MM (1998) A neuro-fuzzy controller for mobile robot navigation and multirobotconvoying. IEEE Trans Syst Man Cyber Part B Cyber 28(6): 829–840CrossRefGoogle Scholar
  25. O’Hara KJ, Walker DB, Balch TR (2008) Physical path planning using a pervasive embedded network. IEEE Trans Robot 24(3): 741–746CrossRefGoogle Scholar
  26. Ordonez C, Collins J, Emmanuel G, Selekwa MF, Dunlap DD (2008) The virtual wall approach to limit cycle avoidance for unmanned ground vehicles. Robot Auton Syst 56(8): 645–657CrossRefGoogle Scholar
  27. Peasgood M, Clark CM, McPhee J (2008) A complete and scalable strategy for coordinating multiple robots within roadmaps. IEEE Trans Robotics 24(2): 283–292CrossRefGoogle Scholar
  28. Pozna C et al (2009) On the design of an obstacle avoiding trajectory: method and simulation. Math Comp Simul doi: 10.1016/j.matcom.2008.12.015
  29. Pradhan SK, Parhi D, Panda AK (2009) Fuzzy logic techniques for navigation of several mobile robots. Appl Soft Comp 9(1): 290–304CrossRefGoogle Scholar
  30. Shibata T, Fukuda T, Tanie K (1993) Fuzzy critic for robotic motion planning by genetic algorithm in hierarchical intelligent control. Proceedings of 1993 international joint conference on neural networks, pp 77–773Google Scholar
  31. Shukla A, Kala R (2008) Multi neuron heuristic search. Int J Comp Sci Network Secur 8(6): 344–350Google Scholar
  32. Shukla A, Tiwari R, Kala R (2008) Mobile robot navigation control in moving obstacle environment using A* algorithm. Intelligent systems engineering systems through artificial neural networks ASME Publications vol 18, pp 113–120Google Scholar
  33. Sud A et al (2008) Real-time path planning in dynamic virtual environments using multiagent navigation graphs. IEEE Trans Visual Comp Graph 14(3): 526–538CrossRefGoogle Scholar
  34. Thrun S (1998) Learning metric-topological maps for indoor mobile robot navigation. Artific Intell 99(1): 21–71CrossRefzbMATHGoogle Scholar
  35. Tsai CH, Lee JS, Chuang JH (2001) Path planning of 3-D objects using a new workspace model. IEEE Trans Syst Man Cyber Part C Appl Rev 31(3): 405–410CrossRefGoogle Scholar
  36. Urdiales C, Bantlera A, Arrebola F, Sandoval F (1998) Multi-level path planning algorithm for autonomous robots. IEEE Elec Lett 34(2): 223–224CrossRefGoogle Scholar
  37. Xiao J, Michalewicz Z, Zhang L, Trojanowski K (1997) Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans Evol Comp 1(1): 18–28CrossRefGoogle Scholar
  38. Zhu D, Latombe JC (1991) New heuristic algorithms for efficient hierarchical path planning. IEEE Trans Robot Autom 7(1): 9–20CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Indian Institute of Information Technology and Management GwaliorGwaliorIndia
  2. 2.Indian Institute of Information Technology and Management GwaliorGwaliorIndia

Personalised recommendations