<p>A fundamental requirement for full autonomy for mobile robots is accurate navigation even in situations where satellite navigation or cameras are unavailable. In such practical scenarios, relying only on inertial sensors will result in navigation solution drift due to 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, MoRPI-PINN, a physics-informed neural network framework, has been proposed for inertial-based mobile robot navigation. These investigations are crucial for gaining deeper insights into the mobile robot’s pure inertial navigation solution. By embedding physical laws and constraints into the training process, MoRPI-PINN provides an accurate and improved navigation solution. Using real-world experiments, we show accuracy improvements of over 80% compared to other baseline approaches for unseen trajectories. Moreover, MoRPI-PINN is a lightweight approach that can be implemented even on edge devices and used in any typical mobile robot application.</p>