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Risk-Aware Motion Planning and Control Using CVaR-Constrained Optimization
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hakobyan, Astghik | - |
dc.contributor.author | Kim, Gyeong Chan | - |
dc.contributor.author | Yang, Insoon | - |
dc.date.accessioned | 2023-07-11T01:38:37Z | - |
dc.date.available | 2023-07-11T01:38:37Z | - |
dc.date.created | 2020-03-30 | - |
dc.date.created | 2020-03-30 | - |
dc.date.issued | 2019-10 | - |
dc.identifier.citation | IEEE Robotics and Automation Letters, Vol.4 No.4, pp.3924-3931 | - |
dc.identifier.issn | 2377-3766 | - |
dc.identifier.uri | https://hdl.handle.net/10371/195096 | - |
dc.description.abstract | We propose a risk-aware motion planning and decision-making method that systematically adjusts the safety and conservativeness in an environment with randomly moving obstacles. The key component of this method is the conditional value-at-risk (CVaR) used to measure the safety risk that a robot faces. Unlike chance constraints, CVaR constraints are coherent, convex, and distinguish between tail events. We propose a two-stage method for safe motion planning and control: A reference trajectory is generated by using RRT* in the first stage, and then a receding horizon controller is employed to limit the safety risk by using CVaR constraints in the second stage. However, the second stage problem is nontrivial to solve, as it is a triple-level stochastic program. We develop a computationally tractable approach through 1) a reformulation of the CVaR constraints; 2) a sample average approximation; and 3) a linearly constrained mixed integer convex program formulation. The performance and utility of this risk-aware method are demonstrated via simulation using a 12-dimensional model of quadrotors. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Risk-Aware Motion Planning and Control Using CVaR-Constrained Optimization | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/LRA.2019.2929980 | - |
dc.citation.journaltitle | IEEE Robotics and Automation Letters | - |
dc.identifier.wosid | 000480311300022 | - |
dc.identifier.scopusid | 2-s2.0-85089937946 | - |
dc.citation.endpage | 3931 | - |
dc.citation.number | 4 | - |
dc.citation.startpage | 3924 | - |
dc.citation.volume | 4 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Yang, Insoon | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | VALUE-AT-RISK | - |
dc.subject.keywordPlus | APPROXIMATION | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordAuthor | Optimization and optimal control | - |
dc.subject.keywordAuthor | probability and statistical methods | - |
dc.subject.keywordAuthor | robot safety | - |
dc.subject.keywordAuthor | collision avoidance | - |
dc.subject.keywordAuthor | motion and path planning | - |
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