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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.20518 |
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| _version_ | 1866914007183523840 |
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| author | Vlasak, Jiri Sojka, Michal Hanzálek, Zdeněk |
| author_facet | Vlasak, Jiri Sojka, Michal Hanzálek, Zdeněk |
| contents | Automated parking is a self-driving feature that has been in cars for several years. Parking assistants in currently sold cars fail to park in more complex real-world scenarios and require the driver to move the car to an expected starting position before the assistant is activated. We overcome these limitations by proposing a planning algorithm consisting of two stages: (1) a geometric planner for maneuvering inside the parking slot and (2) a Rapidly-exploring Random Trees (RRT)-based planner that finds a collision-free path from the initial position to the slot entry. Evaluation of computational experiments demonstrates that improvements over commonly used RRT extensions reduce the parking path cost by 21 % and reduce the computation time by 79.5 %. The suitability of the algorithm for real-world parking scenarios was verified in physical experiments with Porsche Cayenne. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_20518 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving Rapidly-exploring Random Trees algorithm for Automated Parking in Real-world Scenarios Vlasak, Jiri Sojka, Michal Hanzálek, Zdeněk Robotics Automated parking is a self-driving feature that has been in cars for several years. Parking assistants in currently sold cars fail to park in more complex real-world scenarios and require the driver to move the car to an expected starting position before the assistant is activated. We overcome these limitations by proposing a planning algorithm consisting of two stages: (1) a geometric planner for maneuvering inside the parking slot and (2) a Rapidly-exploring Random Trees (RRT)-based planner that finds a collision-free path from the initial position to the slot entry. Evaluation of computational experiments demonstrates that improvements over commonly used RRT extensions reduce the parking path cost by 21 % and reduce the computation time by 79.5 %. The suitability of the algorithm for real-world parking scenarios was verified in physical experiments with Porsche Cayenne. |
| title | Improving Rapidly-exploring Random Trees algorithm for Automated Parking in Real-world Scenarios |
| topic | Robotics |
| url | https://arxiv.org/abs/2310.20518 |