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2D UAV Path Planning with Radar Threatening Areas using Simulated Annealing Algorithm for Event Detection

机译:利用模拟退火算法进行雷达威胁区域的二维无人机路径规划以进行事件检测

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Path Planning for Unmanned Aerial Vehicles (UAVs) can be used for many purposes. However, the problem becomes more and more complex when dealing with a large number of points to visit for detecting and catching different type of events and simple threat avoidance such as Radar Areas. In the literature different type of algorithms (especially evolutionary algorithms) are preferred. In this project, Simulated Annealing (SA) Algorithm is used for solving the path planning problem. Firstly, problem is converted to a part of Travelling Salesman Problem (TSP), and then the solutions are optimized with the 2-Opt approach and other simple algorithms. The code is implemented in MATLAB by using its visualization. Circular avoidance approach is developed and applied with the Simulated Annealing in order to escape from circular radar threats. Tests have been made to observe the results of SA algorithm and radar threats avoidance approaches, where the results show that after a period of time, SA algorithm gives acceptable solutions with the capacities of escaping from radar area threats. Where SA algorithm gives better solutions in less period of time when there are no radar threats. Experimental results depicted that the proposed model can result in an acceptable solution for UAVs in sufficient execution time. This model can be used as an alternative solution to the similar evolutionary algorithms.
机译:无人机的路径规划(UAV)可以用于许多目的。但是,当处理大量要访问的要点以检测和捕获不同类型的事件以及诸如雷达区之类的简单威胁规避时,问题变得越来越复杂。在文献中,不同类型的算法(尤其是进化算法)是优选的。在该项目中,模拟退火(SA)算法用于解决路径规划问题。首先,将问题转换为Traveling Salesman Problem(TSP)的一部分,然后使用2-Opt方法和其他简单算法对解决方案进行优化。该代码通过使用可视化在MATLAB中实现。开发了圆形回避方法,并将其与模拟退火配合使用,以逃避圆形雷达威胁。已经进行了测试以观察SA算法和雷达威胁规避方法的结果,结果表明,经过一段时间后,SA算法给出了具有避开雷达区域威胁能力的可接受的解决方案。在没有雷达威胁的情况下,SA算法可以在较短的时间内提供更好的解决方案。实验结果表明,所提出的模型可以在足够的执行时间内为无人机提供可接受的解决方案。该模型可以用作类似进化算法的替代解决方案。

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