CASIA OpenIR  > 离退休人员
Multiobjective Optimal Parking Maneuver Planning of Autonomous Wheeled Vehicles
Chai, Runqi1,2; Tsourdos, Antonios1; Savvaris, Al1; Chai, Senchun; Xia, Yuanqing2; Chen, C. L. Philip3,4,5
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN0278-0046
2020-12-01
Volume67Issue:12Pages:10809-10821
Corresponding AuthorChai, Runqi(r.chai@cranfield.ac.uk)
AbstractThis article proposes a computational trajectory optimization framework for solving the problem of multiobjective automatic parking motion planning. Constrained automatic parking maneuver problem is usually difficult to solve because of some practical limitations and requirements. This problem becomes more challenging when multiple objectives are required to be optimized simultaneously. The designed approach employs a swarm intelligent algorithm to produce the tradeoff front along the objective space. In order to enhance the local search ability of the algorithm, a gradient operation is utilized to update the solution. In addition, since the evolutionary process tends to be sensitive with respect to the flight control parameters, a novel adaptive parameter controller is designed and incorporated in the algorithm framework such that the proposed method can dynamically balance the exploitation and exploration. The performance of using the designed multiobjective strategy is validated and analyzed by performing a number of simulation and experimental studies. The results indicate that the present approach can provide reliable solutions and it can outperform other existing approaches investigated in this article.
KeywordOptimization Trajectory Planning Autonomous vehicles Wheels Acceleration Adaptation models Adaptive parameter controller automatic parking trajectory optimization tradeoff front
DOI10.1109/TIE.2019.2962482
WOS KeywordPARTICLE SWARM OPTIMIZATION ; TRAJECTORY OPTIMIZATION ; EVOLUTIONARY ; TRACKING
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000564342400078
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:45[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41511
Collection离退休人员
Corresponding AuthorChai, Runqi
Affiliation1.Cranfield Univ, Sch Aerosp Transport & Mfg, Cranfield MK43 0AL, Beds, England
2.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
3.Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China
4.Dalian Maritime Univ, Dept Nav, Dalian 116026, Peoples R China
5.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Chai, Runqi,Tsourdos, Antonios,Savvaris, Al,et al. Multiobjective Optimal Parking Maneuver Planning of Autonomous Wheeled Vehicles[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2020,67(12):10809-10821.
APA Chai, Runqi,Tsourdos, Antonios,Savvaris, Al,Chai, Senchun,Xia, Yuanqing,&Chen, C. L. Philip.(2020).Multiobjective Optimal Parking Maneuver Planning of Autonomous Wheeled Vehicles.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,67(12),10809-10821.
MLA Chai, Runqi,et al."Multiobjective Optimal Parking Maneuver Planning of Autonomous Wheeled Vehicles".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 67.12(2020):10809-10821.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chai, Runqi]'s Articles
[Tsourdos, Antonios]'s Articles
[Savvaris, Al]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chai, Runqi]'s Articles
[Tsourdos, Antonios]'s Articles
[Savvaris, Al]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chai, Runqi]'s Articles
[Tsourdos, Antonios]'s Articles
[Savvaris, Al]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.