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This paper presents a method to determine the route of a three-dimensional UAV. Three criteria; the height, the length of flight path and the un authorized areas are used as the constraints and combined in a fuzzy function as the evaluation function. The article aimed to discover a minimum cost route from source to destination considering the constrains. In this paper a new searching methodis proposed, with use of auxiliary points. The auxiliary point method iterativelydivides a straight line to two shorter lines with less cost of evaluation function. Implementation results show that the proposed method dramatically decreasesthe calculations; meanwhile the ight route is sub-optimum. Keyword:Evaluation Fuzzy systems Three-dimensional routing UAV Copyright © 2017 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Abbas Fadavi, Department of Mechatronics, Science and Research Branch, Islamic Azad Univercity, Semnan, Iran Email: abbas_fadavi@yahoo.com INTRODUCTIONUVA has its application mainly in the military industry [1][2][3]. However, it is used in a large number of non-civilian applications, such as fire fighting, meteorology, aerial photography and research projects. The aim of this article is to discover an appropriate route for the UAVs. An optimum route is a minimum distance route one which best satisfies the constraints. Different methods have been introduced and discussed in order to determine the optimal auxiliarypoints. Various articles have been presented to determine the optimal route forthe UVAs. A number of these articles have made use of classic methods. [11] applied hierarchical decision making. Their, the intended environment was first examined and divided into different areas. Then, according to the rate of the constraints satisfaction problems, the best identification areas and the paths were specified inorder to be able to reach the destination from the source among optimum zonesand to discover the best path. The method of Dijkstra was used. Robergeetal [12] presented a definite cost function. This function utilized a combination of different constraints like distance length with dangerous points and the rate of fuel consumption. Then, the optimal path was calculated using genetic algorithms or PSO based on minimizing or maximizing the cost function. Also, researchers like Kok et al [13] applied executive points to their hardware routing implementation. In article [11] the environment was first examined and divided into different zones. Then, optimal zones were identified. These are the zones which better satisfied the points inside the zones of