dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
Seattle, Washington |
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dc.citation.endPage |
4201 |
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dc.citation.startPage |
4195 |
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dc.citation.title |
IEEE International Conference on Robotics and Automation |
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dc.contributor.author |
Jeon, Jeong hwan |
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dc.contributor.author |
Karaman, Sertac |
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dc.contributor.author |
Frazzoli, Emilio |
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dc.date.accessioned |
2023-12-19T22:36:17Z |
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dc.date.available |
2023-12-19T22:36:17Z |
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dc.date.created |
2019-08-21 |
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dc.date.issued |
2015-05-26 |
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dc.description.abstract |
The RRT∗ algorithm has efficiently extended Rapidly-exploring Random Trees (RRTs) to endow it with asymptotic optimality. We propose Goal-Rooted Feedback Motion Trees (GR-FMTs) that honor state/input constraints and generate collision-free feedback policies. Given analytic solutions for optimal local steering, GR-FMTs obtain and realize safe, dynamically feasible, and asymptotically optimal trajectories toward goals. Second, for controllable linear systems with linear state/input constraints, we propose a fast method for local steering, based on polynomial basis functions and segmentation. GR-FMTs with the method obtain and realize trajectories that are collision-free, dynamically feasible under constraints, and asymptotically optimal within a set we define. The formulation includes linear or quadratic programming of small sizes, where constraints are identified by root-finding in low or medium order of polynomials and added progressively. |
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dc.identifier.bibliographicCitation |
IEEE International Conference on Robotics and Automation, pp.4195 - 4201 |
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dc.identifier.doi |
10.1109/ICRA.2015.7139777 |
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dc.identifier.issn |
1050-4729 |
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dc.identifier.scopusid |
2-s2.0-84938267643 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/32624 |
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dc.identifier.url |
https://ieeexplore.ieee.org/document/7139777 |
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dc.language |
영어 |
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dc.publisher |
IEEE |
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dc.title |
Optimal Sampling-Based Feedback Motion Trees among Obstacles for Controllable Linear Systems with Linear Constraints |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2015-05-26 |
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