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A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots
Li YK(李元凯); Ding, Liang; Zheng, Zhizhong; Yang, Qizhi; Zhao XG(赵新刚); Liu GJ(刘光军)
Department机器人学研究室
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
2018
Volume104Pages:758-775
Indexed BySCI ; EI
EI Accession number20175104547341
WOS IDWOS:000423652800049
Contribution Rank1
Funding OrganizationNatural Sciences and Engineering Research Council (NSERC) ; Canada Research Chair (CRC) ; China State Key Laboratory of Robotics ; SRF ; SEM ; China Fundamental Research Funds for the Central Universities
KeywordTerrain Parameters Real-time Estimation Multi-mode Recursive Gauss-newton Method Adaptive Robust Extended Kalman Filter
AbstractFor motion control of wheeled planetary rovers traversing on deformable terrain, real-time terrain parameter estimation is critical in modeling the wheel-terrain interaction and compensating the effect of wheel slipping. A multi-mode real-time estimation method is proposed in this paper to achieve accurate terrain parameter estimation. The proposed method is composed of an inner layer for real-time filtering and an outer layer for online update. In the inner layer, sinkage exponent and internal frictional angle, which have higher sensitivity than that of the other terrain parameters to wheel-terrain interaction forces, are estimated in real time by using an adaptive robust extended Kalman filter (AREKF), whereas the other parameters are fixed with nominal values. The inner layer result can help synthesize the current wheel-terrain contact forces with adequate precision, but has limited prediction capability for time-variable wheel slipping. To improve estimation accuracy of the result from the inner layer, an outer layer based on recursive Gauss-Newton (RGN) algorithm is introduced to refine the result of real-time filtering according to the innovation contained in the history data. With the two-layer structure, the proposed method can work in three fundamental estimation modes: EKF, REKF and RGN, making the method applicable for flat, rough and non-uniform terrains. Simulations have demonstrated the effectiveness of the proposed method under three terrain types, showing the advantages of introducing the two-layer structure.
Language英语
WOS SubjectEngineering, Mechanical
WOS KeywordPLANETARY EXPLORATION ROVERS ; DEFORMABLE ROUGH TERRAIN ; INTERACTION-MODEL ; SOIL ; TRACKING ; FILTER ; IDENTIFICATION ; VEHICLE
WOS Research AreaEngineering
Funding ProjectNatural Sciences and Engineering Research Council (NSERC) ; Canada Research Chair (CRC) ; China State Key Laboratory of Robotics[2013-O08] ; SRF ; SEM ; China Fundamental Research Funds for the Central Universities[ZYGX-2014-J098]
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21472
Collection机器人学研究室
Corresponding AuthorLi YK(李元凯)
Affiliation1.School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
2.Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China
3.State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
4.National Engineering & Technology Research Center for Digital Switching System, Zhengzhou, China
5.School of Mechanical Engineering, Jiangsu University, Zhenjiang, China
6.Department of Aerospace Engineering, Ryerson University, Toronto, Canada
Recommended Citation
GB/T 7714
Li YK,Ding, Liang,Zheng, Zhizhong,et al. A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots[J]. Mechanical Systems and Signal Processing,2018,104:758-775.
APA Li YK,Ding, Liang,Zheng, Zhizhong,Yang, Qizhi,Zhao XG,&Liu GJ.(2018).A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots.Mechanical Systems and Signal Processing,104,758-775.
MLA Li YK,et al."A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots".Mechanical Systems and Signal Processing 104(2018):758-775.
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