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    出版社： Hamed Shorakaeia1 c1, Mojtaba Vahdania2, Babak Imania3 and Ali. Gholamia4a1 Department of mechanical engineering, Asadabad Branch, Islamic Azad University, Asadabad, Iran
    a2 Imam Hossein University, Tehran, Iran
    a3 Department of mechanical engineering, Harsin Branch, Islamic Azad University, Harsin, Iran
    a4 Imam Hossein University, Tehran, Iran SUMMARY
    The current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chro
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