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Route preparation for unmanned aerial vehicle (UAV) or unmanned aerial system (UAS) is one of the biggest challenges when it comes to carrying out any mission. Analysis of UAS path planning has gained significant attention from researchers as it is an essential aspect of the UAS collaboration project mission. With the objective of solving the path planning issue, we adapt the travelling salesmen problem (TSP) and propose to solve it by operating an improved Genetic Algorithm (GA) that can find out the path within the shortest distance and period. In this paper, we first develop an algorithm using the concept of GA to solve TSP as per the requirements of UAS path planning. The UAS is considered the travelling salesman in the TSP concept and the mission objective is regarded as the visiting city, since TSP is a complex NP-hard problem, this paper performs optimization algorithm as GA to solve TSP, which can be both successfully and expertly planned. Generally, the algorithm used to find the lowest possible distance to visit all of the TSP’s access points. The optimal solution to various challenges tends to require the use of GA. Some conditions and operators that depend on this algorithm are population size, selection method, mutation and crossover. We will present an improved way of the GA operator in this paper. In contrast with several other techniques, the suggested approach for TSP was more successful in the application of UAS path planning. This paper deals with GA as a part of the simulation to represent the TSP model for UAS route planning. The outcomes show that the approach of the GA is relatively effective in finding the minimum path direction. GA is efficient to solve TSP for UAS path planning problem within a reasonable period of less than 1 min for simple problem and less that 5 min for complex problem. |
