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dc.contributor.author오윤선-
dc.contributor.author조경훈-
dc.contributor.author최윤호-
dc.contributor.author오성회-
dc.date.accessioned2021-06-09T04:26:06Z-
dc.date.available2021-06-09T04:26:06Z-
dc.date.issued2020-12-
dc.identifier.citationVOL 온라인게재, 온라인게재-
dc.identifier.issn0018-9286-
dc.identifier.other56510-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/72835-
dc.description.abstractThis paper introduces a robust and safe path planning algorithm in order to satisfy mission requirements specified in linear temporal logic (LTL). When a path is planned to accomplish a mission, it is possible for a robot to fail to complete the mission or collide with obstacles due to noises and disturbances in the system. Hence, we need to find a robust path against possible disturbances. We introduce a robust path planning algorithm, which maximizes the probability of success in accomplishing a given mission by considering disturbances while minimizing the moving distance of a robot. The proposed method can guarantee the safety of the planned trajectory by incorporating an LTL formula and chance constraints in a hierarchical manner. A high-level planner generates a discrete plan satisfying the mission requirements specified in LTL. A low-level planner builds a sampling-based RRT search tree to minimize both the mission failure probability and the moving distance while guaranteeing the probability of collision with obstacles to be below a specified threshold. We have analyzed properties of the proposed algorithm theoretically and validated the robustness and safety of paths generated by the algorithm in simulation and experiments using a quadrotor.-
dc.publisherIEEE transactions on automatic control-
dc.titleChance-constrained multi-layered sampling-based path planning for temporal logic-based missions-
dc.typeArticle-
dc.relation.page온라인게재-
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