How to cite this paper
Jafarzadeh, H., Gholami, S & Bashirzadeh, R. (2014). A new effective algorithm for on-line robot motion planning.Decision Science Letters , 3(1), 121-130.
Refrences
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Changan, L., Xiaohu, Y., Chunyang, L., & Guodong, L. (2010). Dynamic path planning for mobile robot based on improved genetic algorithm. Chinese Journal of Electronics, 19(2), 245-248.
Ghabini, I., & Lan, S. (2002). Adaptation of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks. Transactions on intelligent Transportation Systems; Singapore: IEEE.
Latombe, J. C. (1991). Robot Motion Planning. Kluwer Academic Publishers, Norwell, Massachusetts 02061 USA.
Lee, J.W., Kim, J.J., Chio, B.S., & Lee, J.J. (2008).Improved ant colony optimization algorithm by potential field concept for optimaal path planning. International Conference on Humanoid Robots; Daejeon, Korea: IEEE.
Hu, L., & Gu, Z.Q. (2008). Research and realization of optimum route planning in vehicle navigation systems based on a hybrid genetic algorithm. Journal of Automobile Engineering, 222, 757-763.
Miao, H. (2010). A multi-operator based simulated annealing approach for robot navigation in uncertain environments. International Journal of Computer Science and Security, 4(1), 50-61.
Al-Khatib, M., & saade, J.J. (2003). An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot. Journal of Fuzzy Sets and Systems. 134, 65-82.
Zhu, Q., Hu, J., Cai, W., & Henschen, L. (2011). A new robot naavigation algorithm for dynamic unknown environments based on dynamic path re-computation and an improved scout ant algorithm. Applied Soft computing, 11, 4667-4676.
Changan, L., Xiaohu, Y., Chunyang, L., & Guodong, L. (2010). Dynamic path planning for mobile robot based on improved genetic algorithm. Chinese Journal of Electronics, 19(2), 245-248.
Ghabini, I., & Lan, S. (2002). Adaptation of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks. Transactions on intelligent Transportation Systems; Singapore: IEEE.
Latombe, J. C. (1991). Robot Motion Planning. Kluwer Academic Publishers, Norwell, Massachusetts 02061 USA.
Lee, J.W., Kim, J.J., Chio, B.S., & Lee, J.J. (2008).Improved ant colony optimization algorithm by potential field concept for optimaal path planning. International Conference on Humanoid Robots; Daejeon, Korea: IEEE.
Hu, L., & Gu, Z.Q. (2008). Research and realization of optimum route planning in vehicle navigation systems based on a hybrid genetic algorithm. Journal of Automobile Engineering, 222, 757-763.
Miao, H. (2010). A multi-operator based simulated annealing approach for robot navigation in uncertain environments. International Journal of Computer Science and Security, 4(1), 50-61.
Al-Khatib, M., & saade, J.J. (2003). An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot. Journal of Fuzzy Sets and Systems. 134, 65-82.
Zhu, Q., Hu, J., Cai, W., & Henschen, L. (2011). A new robot naavigation algorithm for dynamic unknown environments based on dynamic path re-computation and an improved scout ant algorithm. Applied Soft computing, 11, 4667-4676.