K.H. Sedighi,∗ T.W. Manikas,∗∗ K. Ashenayi,∗∗∗ and R.L. Wainwright∗∗∗∗


  1. [1] J.C. Latombe, Robot motion planning (Boston, USA: Kluwer Academic Publishers, 1991).
  2. [2] S.S. Ge, X. Lai, & A.A. Mamun, Boundary following and globally convergent path planning using instant goals, IEEE Transactions on Systems, Man and Cybernetics, Part B, 35(2), 2005, 240–254.
  3. [3] C. Hocaoglu & A.C. Sanderson, Planning multiple paths with evolutionary speciation, IEEE Transactions on Evolutionary Computation, 5(3), 2001, 169–191.
  4. [4] A. Howard, H. Seraji, & B. Werger, Global and regional path planners for integrated planning and navigation, Journal of Robotic Systems, 22(12), 2005, 767–778.
  5. [5] R. Huq, G.K.I. Mann, & R.G. Gosine, Behavior-modulation technique in mobile robotics using fuzzy discrete event system, IEEE Transactions on Robotics, 22(5), 2006, 903–916.
  6. [6] E. Masehian & D. Sedighizadeh, Classic and heuristic approaches in robot motion planning: A chronological review, Proc. World Academy of Science, Engineering and Technology, 23, 2007, 101–106.
  7. [7] S.S. Ge & Y.J. Cui, New potential functions for mobile robot path planning, IEEE Transactions on Robotics and Automation, 16(5), 2000, 615–620.
  8. [8] Y.K. Hwang & N. Ahuja, Gross motion planning: A survey, ACM Computing Surveys, 24(3), 1992, 219–291.
  9. [9] J.Y. Hwang, J.S. Kim, S.S. Lim, & K.H. Park, A fast path planning by path graph optimization, IEEE Transactions on Systems, Man and Cybernetics, Part A, 33(1), 2003, 121–129.
  10. [10] B. Stilman, Network languages for complex systems, Computers & Mathematics with Applications, 26(8), 1993, 51–79.
  11. [11] R. Glasius, A. Komoda, & S.C.A.M. Gielen, Neural network dynamics for path planning and obstacle avoidance, Neural Networks, 8(1), 1995, 125–133.
  12. [12] S.X. Yang & C. Luo, A neural network approach to complete coverage path planning, IEEE Transactions on Systems, Man and Cybernetics, Part B, 34(1), 2004, 718–724.
  13. [13] E.A. Antonelo, B. Schrauwen, & D. Stroobandt, Event detection and localization for small mobile robots using reservoir computing, Neural Networks, 21(6), 2008, 862–871.
  14. [14] A. Zou, Z. Hou, L. Zhang, & M. Tan, A neural networkbased camera calibration method for mobile robot localization problems, Proc. 2nd International Symp. on Neural Networks, Chongquin, China, 2005, 277–284.
  15. [15] A. Alvarez, A. Caiti, & R. Onken, Evolutionary path planning for autonomous underwater vehicles in a variable ocean, IEEE Journal of Oceanic Engineering, 29(2), 2004, 418–429.
  16. [16] M. Mitchell, An introduction to genetic algorithms (Cambridge, USA: MIT Press, 1996).
  17. [17] K. Sugihara & J. Smith, Genetic algorithms for adaptive motion planning of an autonomous mobile robot, Proc. 1997 IEEE International Symp. on Computational Intelligence in Robotics and Automation (CIRA ’97), Monterey, CA, 1997, 138–143.
  18. [18] T. Geisler & T.W. Manikas, Autonomous robot navigation system using a novel value encoded genetic algorithm, 45th IEEE International Midwest Symp. on Circuits and Systems, Tulsa, OK, 2002, 45–48.
  19. [19] A. Hermanu, T.W. Manikas, K. Ashenayi, & R.L. Wainwright, Autonomous robot navigation using a genetic algorithm with an efficient genotype structure, in C.H. Dagli, A.L. Buczak, D.L. Enke, M.J. Embrechts, & O. Ersoy, (Eds.), Intelligent engineering systems through artificial neural networks: Smart engineering systems design: Neural networks, fuzzy logic, evolutionary programming, complex systems and artificial life, 14 (New York: ASME Press, 2004), 319–324.
  20. [20] K.H Sedighi, K. Ashenayi, T.W. Manikas, R.L. Wainwright, & H.M. Tai, Autonomous local path planning for a mobile robot using a genetic algorithm, Proc. 2004 IEEE Cong. on 372 Evolutionary Computation (CEC2004), Portland, OR, 2004, 1338–1345.
  21. [21] P. Galiasso & R.L. Wainwright, A hybrid genetic algorithm for the point to multipoint routing problem with single split paths, Proc. 2001 ACM Symp. on Applied Computing (SAC01), Las Vegas, NV, 2001, 327–332.
  22. [22] D. Whitley, The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best, Proc. 3rd International Conf. on Genetic Algorithms, Fairfax, VA, 1989, 116–121.
  23. [23] P.W. Poon & J.N. Carter, Genetic algorithm crossover operators for ordering applications, Computers and Operations Research, 22(1), 1995, 135–147.

Important Links:

Go Back