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Path Planning and Design for AFM based Nano-manipulation using Probability Distribution
Yuan S(袁帅)1,2; Liu LQ(刘连庆)1; Wang ZD(王志东)1; Xing, Jingyi2; Xi N(席宁)1; Wang YC(王越超)1
Department机器人学研究室
Conference NameIEEE International Conference on Cyborg and Bionic Systems (CBS)
Conference DateOctober 17-19, 2017
Conference PlaceBeijing, China
Author of SourceIEEE, IEEE Robot & Automat Soc, IEEE Computat Intelligence Soc, Beijing Inst Technol, IEEE Syst Man & Cybernet Soc, IEEE Engn Med & Biol Soc, IEEE Robot & Automat Soc Tech Comm Neuro Robot Syst
Source Publication2017 IEEE INTERNATIONAL CONFERENCE ON CYBORG AND BIONIC SYSTEMS (CBS)
PublisherIEEE
Publication PlaceNew York
2017
Pages188-193
Indexed ByCPCI(ISTP)
WOS IDWOS:000426790400038
Contribution Rank1
ISBN978-1-5386-3194-2
KeywordAfm Trajectory Planning Nanomanipulation Ant Colony Optimization Dijkstra's Algorithm
Abstract

In AFM nanomanipulation, the uncertainty of tip position in the task space can lead to inefficient nanomanipulation. To resolve this problem, the paper proposes a method which uses the local scan to obtain the observation distance. Then the landmark adjacency matrix is established for designing the observed landmarks. Next, colony optimization and Dijkstra's Algorithm are used to plan the tip trajectory, which reduces the cost greatly. Finally, the experiment results illustrate that the proposed algorithm can perform effective nano-manipulation.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22058
Collection机器人学研究室
Corresponding AuthorYuan S(袁帅)
Affiliation1.State Key Lab. Robot., Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2.Shenyang Jianzhu University, Shenyang 110168, China
Recommended Citation
GB/T 7714
Yuan S,Liu LQ,Wang ZD,et al. Path Planning and Design for AFM based Nano-manipulation using Probability Distribution[C]//IEEE, IEEE Robot & Automat Soc, IEEE Computat Intelligence Soc, Beijing Inst Technol, IEEE Syst Man & Cybernet Soc, IEEE Engn Med & Biol Soc, IEEE Robot & Automat Soc Tech Comm Neuro Robot Syst. New York:IEEE,2017:188-193.
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