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Invariant-Set-Based Planning approach for obstacle avoidance under vehicle dynamic constraints
Qi X(齐欣); Theilliol, Didier; Song DL(宋大雷); Han JD(韩建达)
Department机器人学研究室
Conference Name2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015
Conference DateDecember 6-9, 2015
Conference PlaceZhuhai, China
Source PublicationProceedings of the 2015 IEEE International Conference on Robotics and Biomimetics
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages1692-1697
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161802327773
WOS IDWOS:000380476200282
Contribution Rank1
ISBN978-1-4673-9674-5
AbstractAutonomous vehicles require the ability to safely operate under both external environment constraints such as obstacles, and their own limits such as dynamic conditions. The problem of planning feasible control reference inputs against both constraints is still an open question. In this paper, the contribution is to propose an Invariant-Set-Based Planning (ISBP) approach which can move a vehicle from an initial point to a goal point by calculating feasible control reference inputs with satisfying both constraints simultaneously. A series of invariant sets will be calculated to cover feasible path and system input constraints will be novelly modified to calculate each invariant set. Finally, ISBP approach will be achieved by solving a set of linear matrix inequalities (LMIs).
Language英语
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/18618
Collection机器人学研究室
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation(SIA), Chinese Academy of Sciences(CAS), Shenyang, Liaoning Province, 110016, China
2.CRAN UMR 7039, CNRS, University of Lorraine, Faculte des Sciences et Techniques, B.P. 70239, Vandoeuvre-Les-Nancy, 54506, France
3.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Qi X,Theilliol, Didier,Song DL,et al. Invariant-Set-Based Planning approach for obstacle avoidance under vehicle dynamic constraints[C]. Piscataway, NJ, USA:IEEE,2015:1692-1697.
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