ArticleScientific reports2026
MoRPI-PINN: a physics-informed framework for mobile robot pure inertial navigation.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A Physics-Informed Dual-Branch LSTM Network for UAV Position and Attitude Estimation.Sensors (Basel, Switzerland) · 2026Article
- Fast electromagnetic field simulation using a current-density- based physics-informed neural network.Scientific reports · 2025Article
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Authors and funding
2 authors.
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Abstract
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.
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