用物理约束神经网络提升机器人纯惯性导航精度，抗漂移效果显著。
MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation
- 将物理定律嵌入神经网络训练，约束惯性导航误差
- 实测精度提升超85%，优于现有方法
- 轻量设计可部署于边缘设备，适合各类移动机器人
移动机器人实现完全自主的关键在于，在卫星导航或摄像头不可用的情况下仍能实现精确导航。仅依赖惯性传感器会导致因传感器噪声和误差引起的导航漂移。一种新兴解决方案是让机器人以蛇形蜿蜒运动，提高惯性信号的信噪比，从而实现位置回归。本文提出MoRPI-PINN，一种用于高精度惯性导航的物理信息神经网络框架。通过在训练过程中嵌入物理规律与约束，该框架能够提供准确且鲁棒的导航解。通过真实世界实验验证，相比其他方法，精度提升超过85%。MoRPI-PINN是一种轻量级方法，可在边缘设备上实现，适用于任何典型移动机器人应用。
原文摘要 · Abstract (English)
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.
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