In this paper, design of an autonomous mobile robot (AMR) adapted for robot operating system (ROS) is presented by considering both the hardware architecture and electronic communication protocols. Initially the purpose of the robot has been determined, and then the required components to construct the robot have been obtained. After mounting the electronic and mechanic hardware components on the platform, the required interconnections, data exchange system and software have been installed. Then the required tests have been run on the platform and some solutions methods have been proposed for the problems encountered during the tests. The results obtained and experience gained in the design of the platform have been quite satisfactory and will be a guiding light for future works.

In this paper, the Convolutional Neural Network (CNN) architecture, which is one of the deep learning architectures, is used to classify the basic circuit components drawn by hand. During the training and testing stages of the model, a new dataset containing images of 863 circuit components manually drawn by different people is created. The data set contains images of four different classes of circuit components such as resistor, inductor, capacitor and voltage source. All images have been fixed to the same size and converted to grayscale to increase recognition performance and reduce process complexity. In the study, training for four classes is performed with CNN architecture. Based on the CNN architecture, four new CNN models are employed with different the number of layers. The training and validation results of these models are compared separately, the model with the highest training and validation performance is observed with four layer CNN model (CNN-4). This model obtained 84.41% accuracy rate at classification task.