An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization
In this article, an automatic vehicle parallel parking algorithm, consisting of path planning, controller design, and state estimation is developed. The path is planned using clothoid sequences and a straight line, which avoids stopping the car to reorient the wheels. The control inputs, including s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10124934/ |
| _version_ | 1797818702178025472 |
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| author | Saeede Mohammadi Daniali Alireza Khosravi Pouria Sarhadi Fatemeh Tavakkoli |
| author_facet | Saeede Mohammadi Daniali Alireza Khosravi Pouria Sarhadi Fatemeh Tavakkoli |
| author_sort | Saeede Mohammadi Daniali |
| collection | DOAJ |
| description | In this article, an automatic vehicle parallel parking algorithm, consisting of path planning, controller design, and state estimation is developed. The path is planned using clothoid sequences and a straight line, which avoids stopping the car to reorient the wheels. The control inputs, including speed and steering angle, are a function of traveled distance. This method enables the car to park from different initial poses, achieving reduced parking time and the ability to park in one or two maneuvers, in smaller than standard places. An evolutionary optimization algorithm is used to calculate the best speed parameter according to the defined criteria. The proposed technique utilizes the Unscented Kalman Filter (UKF) to estimate the traveled distance, resulting in a smaller error compared to the conventional Extended Kalman Filter (EKF). The research aims to introduce an optimal automatic parking algorithm to improve the existing methods in terms of parking duration, the required space size for parking in the maximum of two maneuvers, and path continuity. Finally, the fidelity and improved performance of the proposed method are assessed in various probable conditions using the powerful Monte Carlo simulations. |
| first_indexed | 2024-03-13T09:11:57Z |
| format | Article |
| id | doaj.art-39060fc70b994c41924a6fa80f48224a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T09:11:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-39060fc70b994c41924a6fa80f48224a2023-05-26T23:00:49ZengIEEEIEEE Access2169-35362023-01-0111496114962410.1109/ACCESS.2023.327685810124934An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm OptimizationSaeede Mohammadi Daniali0Alireza Khosravi1https://orcid.org/0000-0003-3342-4144Pouria Sarhadi2https://orcid.org/0000-0002-6004-676XFatemeh Tavakkoli3https://orcid.org/0000-0002-4276-0863Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, IranFaculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, IranSchool of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, Hertfordshire, U.KFaculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, IranIn this article, an automatic vehicle parallel parking algorithm, consisting of path planning, controller design, and state estimation is developed. The path is planned using clothoid sequences and a straight line, which avoids stopping the car to reorient the wheels. The control inputs, including speed and steering angle, are a function of traveled distance. This method enables the car to park from different initial poses, achieving reduced parking time and the ability to park in one or two maneuvers, in smaller than standard places. An evolutionary optimization algorithm is used to calculate the best speed parameter according to the defined criteria. The proposed technique utilizes the Unscented Kalman Filter (UKF) to estimate the traveled distance, resulting in a smaller error compared to the conventional Extended Kalman Filter (EKF). The research aims to introduce an optimal automatic parking algorithm to improve the existing methods in terms of parking duration, the required space size for parking in the maximum of two maneuvers, and path continuity. Finally, the fidelity and improved performance of the proposed method are assessed in various probable conditions using the powerful Monte Carlo simulations.https://ieeexplore.ieee.org/document/10124934/Automatic parkingKalman filterMonte Carlo methodoptimizationpath planning |
| spellingShingle | Saeede Mohammadi Daniali Alireza Khosravi Pouria Sarhadi Fatemeh Tavakkoli An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization IEEE Access Automatic parking Kalman filter Monte Carlo method optimization path planning |
| title | An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization |
| title_full | An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization |
| title_fullStr | An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization |
| title_full_unstemmed | An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization |
| title_short | An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization |
| title_sort | automatic parking algorithm design using multi objective particle swarm optimization |
| topic | Automatic parking Kalman filter Monte Carlo method optimization path planning |
| url | https://ieeexplore.ieee.org/document/10124934/ |
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