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Stochastic mapping for chemical plume source localization with application to autonomous hydrothermal vent discovery

机译:化学羽流源定位的随机映射应用于自主热液孔发现

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摘要

This thesis presents a stochastic mapping framework for autonomous robotic chemical plume source localization in environments with multiple sources. Potential applications for robotic chemical plume source localization include pollution and environmental monitoring, chemical plant safety, search and rescue, anti-terrorism, narcotics control, explosive ordinance removal, and hydrothermal vent prospecting. Turbulent flows make the spatial relationship between the detectable manifestation of a chemical plume source, the plume itself, and the location of its source inherently uncertain. Search domains with multiple sources compound this uncertainty because the number of sources as well as their locations is unknown a priori. Our framework for stochastic mapping is an adaptation of occupancy grid mapping where the binary state of map nodes is redefined to denote either the presence (occupancy) or absence of an active plume source. A key characteristic of the chemical plume source localization problem is that only a few sources are expected in the search domain. The occupancy grid framework allows for both plume detections and non-detections to inform the estimated state of grid nodes in the map, thereby explicitly representing explored but empty portions of the domain as well as probable source locations.
机译:本文提出了一种用于多源环境中自主机器人化学羽源定位的随机映射框架。机器人化学羽流源本地化的潜在应用包括污染和环境监测,化工厂安全,搜索和救援,反恐,麻醉品控制,爆炸性法规清除以及热液喷口勘探。湍流使化学烟羽源的可检测到的表现形式,烟羽本身与其源的位置之间的空间关系固有地不确定。具有多个源的搜索域加剧了这种不确定性,因为先验未知源的数量及其位置。我们的随机映射框架是对占用网格映射的一种适应,其中重新定义了映射节点的二进制状态,以表示活动羽状源的存在(占用)或不存在。化学羽流源定位问题的一个关键特征是,在搜索域中预期只有少数几个源。占用网格框架允许进行羽流检测和不进行检测,以告知地图中网格节点的估计状态,从而明确表示域的已探查但为空的部分以及可能的源位置。

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