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.

中文翻译：

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使用递归神经网络的智能移动机器人命令预测器

摘要 由于新的先进技术的发展，通过经典神经网络（NN）模型的移动机器人自主导航在效率和准确性方面不再有效。然而，由于轨迹的非线性和复杂性，寻找可实现的递归神经网络 (RNN) 模型来预测具有速度和精度约束的机器人的电机控制的必要性仍然停滞不前。为了为智能导航问题提供新的解决方案，本文提出了一种新的可实现递归神经网络控制器 (RNNC) 预测器，用于计算电机的脉冲宽度调制 (PMW) 信号。这种提出的多输入多输出（MIMO）控制器成功地解决了自主导航的速度和准确性问题。详细说明了智能 RNNC 模型设计及其架构。由于现实世界中训练过程的复杂性和低效率，开发了一个 3D 模拟器来创建所有可能的场景。详细介绍了用于设计新 RNNC 模型的机器学习和导航预测过程。此外，电机指令生成速度和准确性以及它们的效率在理论上和实践中都得到了验证。此外，数值研究、轨迹跟踪和避障的 3D 场景证明了所提出技术的有效性和鲁棒性。详细介绍了用于设计新 RNNC 模型的机器学习和导航预测过程。此外，电机指令生成速度和准确性以及它们的效率在理论上和实践中都得到了验证。此外，数值研究、轨迹跟踪和避障的 3D 场景证明了所提出技术的有效性和鲁棒性。详细介绍了用于设计新 RNNC 模型的机器学习和导航预测过程。此外，电机指令生成速度和准确性以及它们的效率在理论上和实践中都得到了验证。此外，数值研究、轨迹跟踪和避障的 3D 场景证明了所提出技术的有效性和鲁棒性。