Modular robots are flexible structures that offer versatility and configuration options for carrying out different types of movements; however, disconnection problems between the modules can lead to the loss of information, and, therefore, the proposed displacement objectives are not met. This work proposes the control of a chain-type modular robot using an artificial neural network (ANN) that enables the robot to go through different environments. The main contribution of this research is that it uses a software defined radio (SDR) system, where the Wi-Fi channel with the best signal-to-noise Ratio (SNR) is selected to send the information regarding the simulated movement parameters and obtained by the controller to the modular robot. This allows for faster communication with fewer errors. In case of a disconnection, these parameters are stored in the simulator, so they can be sent again, which increases the tolerance to communication failures. Additionally, the robot sends information about the average angular velocity, which is stored in the cloud. The errors in the ANN controller results, in terms of the traveled distance and time estimated by the simulator, are less than 6% of the real robot values.