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Improve RRT Algorithm for Path Planning in Complex Environments
Zhang W(张伟)1,2; Yi CL(易春林)3; Gao S(高升)1,2; Zhang ZW(张赵威)1,2; He X(何旭)1,2
Department空间自动化技术研究室
Conference Name39th Chinese Control Conference, CCC 2020
Conference DateJuly 27-29, 2020
Conference PlaceShenyang, China
Author of SourceSystems Engineering Society of China (SESC) ; Technical Committee on Control Theory (TCCT) of Chinese Association of Automation (CAA)
Source PublicationProceedings of the 39th Chinese Control Conference, CCC 2020
PublisherIEEE Computer Society
Publication PlaceWashington, USA
2020
Pages3777-3782
Indexed ByEI
EI Accession number20203909241713
Contribution Rank1
ISSN1934-1768
ISBN978-9-8815-6390-3
KeywordOptimal path RRT* algorithm RRT algorithm Path planning
AbstractThis paper proposes an improved RRT algorithm, which overcomes the problems of non-optimal path and low planning success rate in complex environments. First, the algorithm introduces a memory strategy that avoids the problem of oversampling or falling into local minimum in the non-convex environment during the process of expanding random tree. Then, the heuristic function is used to guide the growth of random tree and neighborhood expansion strategy is used to avoid the "rewiring" process of RRT* algorithm, which improves the real-time planning performance. Finally, simulation experiments are performed in different types of maps to illustrate the effectiveness of the proposed algorithm.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27837
Collection空间自动化技术研究室
Corresponding AuthorZhang W(张伟)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.Shenyang Ligong University, Shenyang 110159, China
Recommended Citation
GB/T 7714
Zhang W,Yi CL,Gao S,et al. Improve RRT Algorithm for Path Planning in Complex Environments[C]//Systems Engineering Society of China (SESC), Technical Committee on Control Theory (TCCT) of Chinese Association of Automation (CAA). Washington, USA:IEEE Computer Society,2020:3777-3782.
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