Autonomous exploration of large-scale benthic environments
Autonomous exploration of large-scale benthic environments
复制标题
大规模海底环境自主探索
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
O. Pizarro
中科院分区：
文献类型：
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作者：
A. Bender;Stefan B. Williams;O. Pizarro
成熟的技术使机器人能够可靠地部署到大规模环境中进行监测和勘探应用。忽略运输过程中收集的信息价值的规划技术能够在这些环境中有效地运行，并在指定的起点和终点之间生成轨迹。包括在运输过程中收集的信息的价值增加了问题的复杂性，并经常导致算法无法扩展到大型环境。本文提出了一种在大规模未开发环境中规划信息调查的方法。所提出的方法不需要起始或结束位置作为约束。相反，机器人操作员需要指定一个调查模板，以满足车辆约束和部署的科学目标。这个约束将勘探问题转化为一个实验设计问题，其目标是为指定的测量轨迹选择一个位置。使用高斯过程学习调查实用程序的函数表示。该模型允许在连续空间和任意位置查询候选调查位置的效用。该方法在海洋数据上得到了验证。目的是设计一项调查，以便准确估计大型海洋环境中栖息地的空间分布。结果表明，所提出的勘探方法能够成功地对隐藏测量效用函数进行建模，并推荐信息丰富的测量位置。
Maturing technology has allowed the reliable deployment of robots into large-scale environments for monitoring and exploration applications. Planning techniques which ignore the value of information gathered during transit are able to operate efficiently in these environments and generate trajectories between specified starting and ending locations. Including the value of information gathered during transit increases the complexity of the problem and often leads to algorithms which are unable to scale up to large environments. This paper presents a method for planning informative surveys in large-scale unexplored environments. The proposed methodology does not require a starting or ending location as a constraint. Instead, robot operators are required to specify a survey template, which satisfies both vehicle constraints and the scientific objectives of the deployment. This constraint converts the exploration problem into an experimental design problem where the objective is to choose a location for the specified survey trajectory. A functional representation of the survey utility is learnt using a Gaussian process. This model allows the utility of candidate survey placements to be queried in a continuous space and in arbitrary locations. The proposed exploration method is demonstrated and validated on marine data. The objective is to design a survey which allows the spatial distribution of habitats in a large marine environment to be estimated accurately. The results show that the proposed exploration method is able to model the hidden survey utility function successfully and recommend informative survey placements.