Outdoor Localization and Path Planning for Repositioning an Autonomous Electric Scooter

This paper describes the development of a self-driving e-scooter with the ability to safely travel without a rider along a pre-planned route using automated onboard control. Ride-share electric scooters are an alternative to walking or driving, but they are often ineffective or a nuisance due to their placement after usage. An autonomous driving framework specific to an e-scooter, including path planning and state estimation, enables short-distance repositioning and parking. This paper describes the framework's hardware and software components along with design choices and pitfalls. We also present results of real-world autonomous navigation, including the effectiveness of our localization methods.

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Publication Details

Journal
IEEE Transactions on Intelligent Vehicles
Published
2024-10-09
DOI
https://doi.org/10.1109/tiv.2024.3477271
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.25
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article

Outdoor Localization and Path Planning for Repositioning an Autonomous Electric Scooter

J.-J. Lee, Derek A. Paley, Srijal S. Poojari
1 citations
IEEE Transactions on Intelligent Vehicles
Robotic Path Planning Algorithms
0.25
article

Outdoor Localization and Path Planning for Repositioning an Autonomous Electric Scooter

J.-J. Lee, Derek A. Paley, Srijal S. Poojari
article en
1 citations

Abstract

This paper describes the development of a self-driving e-scooter with the ability to safely travel without a rider along a pre-planned route using automated onboard control. Ride-share electric scooters are an alternative to walking or driving, but they are often ineffective or a nuisance due to their placement after usage. An autonomous driving framework specific to an e-scooter, including path planning and state estimation, enables short-distance repositioning and parking. This paper describes the framework's hardware and software components along with design choices and pitfalls. We also present results of real-world autonomous navigation, including the effectiveness of our localization methods.

IEEE Transactions on Intelligent VehiclesVol. 10(7)
University of Maryland, College Park (US)
Sustainable cities and communities
Openalex Percentile: Top 46%
Robotic Path Planning Algorithms
0.25
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