The Global Shortest Path Visualization Approach with Obstructions
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- Dong Guan-Qiang
- Institute of Systems Science and Engineering, Henan Engineering Laboratory of Wind Power Systems, Henan University of Science and Technology
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- Yang Zong-Xiao
- Institute of Systems Science and Engineering, Henan Engineering Laboratory of Wind Power Systems, Henan University of Science and Technology
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- Song Lei
- Institute of Systems Science and Engineering, Henan Engineering Laboratory of Wind Power Systems, Henan University of Science and Technology
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- Ye Kun
- Institute of Systems Science and Engineering, Henan Engineering Laboratory of Wind Power Systems, Henan University of Science and Technology
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- Li Gen-Sheng
- Institute of Systems Science and Engineering, Henan Engineering Laboratory of Wind Power Systems, Henan University of Science and Technology
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Description
<p>The avoidance obstacle path planning problem is stated in an obstacle environment. The minimum Steiner tree theory is the basis of the global shortest path. It is one of the classic NP-hard problem in nonlinear combinatorial optimization. A visualization experiment approach has been used to find Steiner point and system’s shortest path is called Steiner minimum tree. However, obstacles must be considered in some problems. An Obstacle Avoiding Steiner Minimal Tree (OASMT) connects some points and avoids running through any obstacle when constructing a tree with a minimal total length. We used a geometry experiment approach (GEA) to solve OASMT by using the visualization experiment device discussed below. A GEA for some systems with obstacles is used to receive approximate optimizing results. We proved the validity of the GEA for the OASMT by solving problems in which the global shortest path is obtained successfully by using the GEA.</p>
Journal
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- Journal of Robotics and Mechatronics
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Journal of Robotics and Mechatronics 27 (5), 579-585, 2015-10-20
Fuji Technology Press Ltd.
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Keywords
Details 詳細情報について
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- CRID
- 1390001288149499264
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- NII Article ID
- 130007671233
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- NII Book ID
- AA10809998
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- ISSN
- 18838049
- 09153942
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- NDL BIB ID
- 026803875
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL Search
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed