A smart mobile robot commands predictor using recursive neural network

Abstract

Autonomous navigation of mobile robot via classic neural network (NN) models are no more valid in terms of efficiency and accuracy due to the development of new advanced techniques. However, the necessity of finding an implementable Recursive Neural Network (RNN) model to predict the motor control of the robot with both speed and accuracy constraints still remains stagnant because of the nonlinearity and complexity of the trajectories.To provide new solutions for smart navigation problems, this paper proposes a new implementable recursive neural network controller (RNNC) predictor that calculates the Pulse Width Modulation (PMW) signals of the motors. Such proposed Multi-input Multi-output (MIMO) Controller succeeded to solve the problem of speed and accuracy of autonomous navigation.The Smart RNNC model design is illustrated with its architecture in details. Due to the complexity and the non-efficiency of the training process in real-world, a 3D Simulator was developed to create all possible scenarios. The machine learning and navigation predictions processes for designing the new RNNC model are presented together in details. In addition, the motor commands generation speed and accuracy as well as their efficiency are theoretically and practically proven. Moreover, numerical studies, 3D scenarios of trajectory tracking and obstacle avoidance prove the effectiveness and robustness of the proposed technique.