MoRPI-PINN：一种用于移动机器人纯惯性导航的物理信息框架
MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation
AI总结：
本文提出MoRPI-PINN框架，通过嵌入物理定律提升移动机器人惯性导航精度，实验证明其比其他方法准确率提升超85%。
摘要：
移动机器人实现完全自主性的关键在于在卫星导航或摄像头不可用时仍能准确导航。仅依赖惯性传感器会导致导航解漂移，因为传感器存在固有噪声和误差。为缓解漂移，可通过蛇形滑动运动提高惯性信号噪声比，从而回归移动机器人位置。本文提出MoRPI-PINN作为物理信息神经网络框架，通过在训练过程中嵌入物理定律和约束，提供准确且稳健的导航解决方案。通过实际实验，我们展示了其比其他方法准确率提升超过85%。MoRPI-PINN是一种轻量级方法，可在边缘设备上实现，并适用于任何典型移动机器人应用。
英文摘要：
A fundamental requirement for full autonomy in mobile robots is accurate navigation even in situations where satellite navigation or cameras are unavailable. In such practical situations, relying only on inertial sensors will result in navigation solution drift due to the sensors' inherent noise and error terms. One of the emerging solutions to mitigate drift is to maneuver the robot in a snake-like slithering motion to increase the inertial signal-to-noise ratio, allowing the regression of the mobile robot position. In this work, we propose MoRPI-PINN as a physics-informed neural network framework for accurate inertial-based mobile robot navigation. By embedding physical laws and constraints into the training process, MoRPI-PINN is capable of providing an accurate and robust navigation solution. Using real-world experiments, we show accuracy improvements of over 85% compared to other approaches. MoRPI-PINN is a lightweight approach that can be implemented even on edge devices and used in any typical mobile robot application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
