Engelli ortamlarda görüntü tabanlı mobil robot kontrolü ve yol planlama algoritmalarının geliştirilmesi

The use of mobile robots is becoming increasingly widespread in the leading sectors, especially in industrial areas, such as services, health, defense, and so on. Recent studies and research have focused on systems that enable the mobile robot to move autonomously. In mobile robot systems, it is crucial to draw up an appropriate (cost-effective and safe) path plan to be followed by the robot and to design an excellent controller to characterize the behavior modeling of the robot. Even though robotic control is made more robust with the developing technology, it should be taken into consideration that the costs of robot control increase. The development of low-cost, high-reliability control systems is one of the topics covered in the field of robot control. These issues were taken into consideration when determining the scope of this thesis. Within the scope of this thesis, the path plan for a mobile robot was realized with the external device configuration and control methods that model mobile robot movements that were developed in this planned path. The environment for simulation and real-time applications was developed using LabVIEW software for mobile robot behavior modeling and out off-device configuration. The external eye configuration applied in the scope of the study is preferred since it does not require the use of internal sensors on the robot. The robot, targets, and obstacles detected in the configuration environment by image processing methods were used to create the environment map. In the generated environment map, a low-cost and safe path was built between the robot and the target without colliding with obstacles using path-planning algorithms. For this purpose, path planning methods are determined. Two path plan methods based on fuzzy logic were designed as Type-1 and Type-2. Rulesets for these path planning methods were created. The proposed path planning methods were compared with the existing path planning algorithms in terms of performance and stability. These methods are A*, Random Branching Trees (RRT), RRT + Dijkstra, Bidirectional Random Branching Trees (B-RRT), B-RRT + Dijkstra, Probabilistic Road Map (PRM), Artificial Potential Area (APF) and Genetic Algorithm ( GA) planning algorithms. In an external eye-type configuration, image-based control approaches, also known as visual servoing, are used. These approaches were used to generate input to the control method according to the global position of the objects on the image obtained from the working environment. To calculate this control input, a distance-based triangular design has been created. With this structure, the robot is controlled according to the distance value of each side of a triangular structure formed between the labels (control points) placed on the robot and the target. Two new methods based on Type-1 and Type-2 fuzzy logic were designed as a control method. Control rule sets were formed and applied to the obtained path plan. To evaluate the performances of the proposed controller, a comparison was made with Gaussian and Decision Tree-based controller method specially designed for visual-based servoing in previous studies. Developed path planning and controller algorithms were tested in five different configuration spaces. In terms of path planning, it is observed that the proposed path planning methods are the best in an average performance in all configuration spaces. Path plan performance was also evaluated by statistical performance metrics in the form of standard deviation, mean, and total error, in particular, the length and execution time of the obtained path plan. Tests were conducted in a real environment for the controller, which characterizes the robot movements to follow the resulting path plan. The controllers designed according to the test results have been more successful in most of the configuration environments than other methods. For the final evaluation, the results between the simulation and the path created by the robot in the real environment were examined, and it was found that the designed methods were remarkably close to the path plans. In this thesis, the mobile robot's go-to goal behavior, obstacle avoidance behavior, and the resulting path plan tracking behavior were successfully modeled in an environment monitored by external eye camera configuration based on a visual servo. According to the results of the study, designed path planning methods, and developed controllers provided significant results that could inspire future studies in this field.