A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization

doi:10.3390/e24081128

	A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization
	Yu, Biao 2; Zhu, Hui 2; Xue, Deyi 1; Xu, Liwei 2; Zhang, Shijin 2; Li, Bichun 2
刊名	ENTROPY
	2022-08-01
卷号	24
关键词	adaptive quantum-inspired evolutionary algorithm dead reckoning intelligent vehicle optimization parameter calibration
DOI	10.3390/e24081128
通讯作者	Li, Bichun(bcli@iim.ac.cn)
英文摘要	Parameter calibration is critical for self-localization based on dead reckoning in the control of intelligent vehicles such as autonomous driving. Most traditional calibration methods for robotics control based on dead reckoning rely on data collection with specially designed paths. For the calibration of parameters in the control of intelligent vehicles, the design of such paths is considered impossible due to the complexity of road conditions. To solve this problem, an optimization-based dead reckoning calibration scheme is introduced in this research using the differential global positioning system to obtain the actual positions of the intelligent vehicle. In this scheme, the difference between the positions obtained through dead reckoning and the positions obtained through the differential global positioning system is selected as the optimization objective function to be minimized. An adaptive quantum-inspired evolutionary algorithm is developed to improve the quality and efficiency of optimization. Experiments with an intelligent vehicle were also conducted to demonstrate the effectiveness of the developed calibration scheme. In addition, the newly introduced adaptive quantum-inspired evolutionary algorithm is compared with the classic genetic algorithm and the classic quantum-inspired evolutionary algorithm using eight benchmark test functions considering computation quality and efficiency.
资助项目	Youth Innovation Promotion Association of the Chinese Academy of Sciences[Y2021115]
WOS关键词	EXTENDED KALMAN FILTER ; GENETIC ALGORITHM ; MOBILE ROBOT ; SENSORS ; ODOMETRY ; PERFORMANCE ; SYSTEM ; ERROR ; GPS
WOS研究方向	Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000847043700001
资助机构	Youth Innovation Promotion Association of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/131888]
专题	中国科学院合肥物质科学研究院
通讯作者	Li, Bichun
作者单位	1.Univ Calgary, Dept Mech & Mfg Engn, Calgary, AB T2N 1N4, Canada 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Yu, Biao,Zhu, Hui,Xue, Deyi,et al. A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization[J]. ENTROPY,2022,24.
APA	Yu, Biao,Zhu, Hui,Xue, Deyi,Xu, Liwei,Zhang, Shijin,&Li, Bichun.(2022).A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization.ENTROPY,24.
MLA	Yu, Biao,et al."A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization".ENTROPY 24(2022).