DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ha, Jung-Su | ko |
dc.contributor.author | Park, Soon-Seo | ko |
dc.contributor.author | Choi, Han-Lim | ko |
dc.date.accessioned | 2019-03-19T01:50:09Z | - |
dc.date.available | 2019-03-19T01:50:09Z | - |
dc.date.created | 2019-03-11 | - |
dc.date.issued | 2019-03 | - |
dc.identifier.citation | ROBOTICS AND AUTONOMOUS SYSTEMS, v.113, pp.81 - 93 | - |
dc.identifier.issn | 0921-8890 | - |
dc.identifier.uri | http://hdl.handle.net/10203/251782 | - |
dc.description.abstract | This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by developing a topology-guided path integral control method. The path integral control framework, which forms the backbone of the proposed method, re-writes the Hamilton-Jacobi-Bellman equation as a statistical inference problem; the resulting inference problem is solved by a sampling procedure that computes the distribution of controlled trajectories around the trajectory by the passive dynamics. For motion control of robots in a highly cluttered environment, however, this sampling can easily be trapped in a local minimum unless the sample size is very large, since the global optimality of local minima depends on the degree of uncertainty. Thus, a homology-embedded sampling-based planner that identifies many (potentially) local-minimum trajectories in different homology classes is developed to aid the sampling process. In combination with a receding-horizon fashion of the optimal control the proposed method produces a dynamically feasible and collision-free motion plans without being trapped in a local minimum. Numerical examples on a synthetic toy problem and on quadrotor control in a complex obstacle field demonstrate the validity of the proposed method. (C) 2019 Published by Elsevier B.V. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.title | Topology-guided path integral approach for stochastic optimal control in cluttered environment | - |
dc.type | Article | - |
dc.identifier.wosid | 000459358000007 | - |
dc.identifier.scopusid | 2-s2.0-85060074092 | - |
dc.type.rims | ART | - |
dc.citation.volume | 113 | - |
dc.citation.beginningpage | 81 | - |
dc.citation.endingpage | 93 | - |
dc.citation.publicationname | ROBOTICS AND AUTONOMOUS SYSTEMS | - |
dc.identifier.doi | 10.1016/j.robot.2019.01.001 | - |
dc.contributor.localauthor | Choi, Han-Lim | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Stochastic optimal control | - |
dc.subject.keywordAuthor | Topological motion planning | - |
dc.subject.keywordAuthor | Linearly-solvable optimal control | - |
dc.subject.keywordAuthor | Multi-modality | - |
dc.subject.keywordPlus | ALGORITHMS | - |
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