Path-following model predictive control for autonomous e-scooters

David Meister; Robin Strässer; Felix Brändle; Marc Seidel; Benno Bassler; Nathan Gerber; Jan Kautz; Elena Rommel; Frank Allgöwer

arXiv · 2505.05314

Path-following model predictive control for autonomous e-scooters

David Meister·Robin Strässer·Felix Brändle·Marc Seidel·Benno Bassler·Nathan Gerber·Jan Kautz·Elena Rommel·Frank Allgöwer

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Abstract

In order to mitigate economical, ecological, and societal challenges in electric scooter (e-scooter) sharing systems, we develop an autonomous e-scooter prototype. Our vision is to design a fully autonomous prototype that can find its way to the next parking spot, high-demand area, or charging station. In this work, we propose a path-following model predictive control solution to enable localization and navigation in an urban environment with a provided path to follow. We design a closed-loop architecture that solves the localization and path following problem while allowing the e-scooter to maintain its balance with a previously developed reaction wheel mechanism. Our model predictive control approach facilitates state and input constraints, e.g., adhering to the path width, while remaining executable on a Raspberry Pi 5. We demonstrate the efficacy of our approach in a real-world experiment on our prototype.

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David Meister, Robin Strässer, Felix Brändle, Marc Seidel, Benno Bassler, Nathan Gerber, Jan Kautz, Elena Rommel, Frank Allgöwer. 2025-07-09. Path-following model predictive control for autonomous e-scooters. https://arxiv.org/abs/2505.05314

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