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ÖgePath planning with hybrid use of artificial intelligence algorithms in autonomous mobile vehicles(Graduate School, 2022-06-16)It is aimed to solve the path planning problems of autonomous mobile vehicles with ROS support indoor applications. The genetic algorithm was chosen as a path planning algorithm and applied to the robot. The reason for choosing the genetic algorithm is that there are few studies in the literature evaluating the performance of genetic algorithms in environments with dynamic objects. Genetic algorithms are of stochastic algorithms. Stochastic algorithms have to run a large number of tries to plan an optimal path. A high number of attempts requires good processor performance, otherwise, the planning time of the optimal path may be long in environments with dynamic objects. These trials are selected based on the evaluation criteria. The optimal path in this study is marked on the map with waypoints and the vehicle follows these points. This optimal path, which is revealed when all of the points are passed, is the shortest path between the starting point and the goal point. In order to apply the planner to the autonomous mobile robot, the autonomous system architecture must first be created.