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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2505.05314 |
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| _version_ | 1866909680638361600 |
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| author | Meister, David Strässer, Robin Brändle, Felix Seidel, Marc Bassler, Benno Gerber, Nathan Kautz, Jan Rommel, Elena Allgöwer, Frank |
| author_facet | Meister, David Strässer, Robin Brändle, Felix Seidel, Marc Bassler, Benno Gerber, Nathan Kautz, Jan Rommel, Elena Allgöwer, Frank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05314 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Path-following model predictive control for autonomous e-scooters Meister, David Strässer, Robin Brändle, Felix Seidel, Marc Bassler, Benno Gerber, Nathan Kautz, Jan Rommel, Elena Allgöwer, Frank Systems and Control Robotics 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. |
| title | Path-following model predictive control for autonomous e-scooters |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2505.05314 |