论文标题
通过基于低级模型的计划和预测，一种信息理论方法来监视持续环境的环境监测
An Information-Theoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction
论文作者
论文摘要
机器人可用于收集人类难以穿越的地区的环境数据。但是，局限性仍然存在于机器人可以直接观察到每单位时间的区域的大小。我们介绍了一种在大区域中选择有限数量的观察点的方法，我们可以从中预测该地区未观察到的点状态。我们将目标属性的低等级模型与信息最大化路径计划者结合在一起，以预测整个区域的属性状态。我们的方法是选择目标属性和机器人监控平台的不可知论。我们评估了在两个现实世界环境数据集上的模拟方法中，每个方法都包含一个从1到200万个可能的采样位置进行的观察。我们比较了随机抽样器和生态学文献中基线采样器的四个变体。我们的方法在大多数试验中的平均重建误差方面的平均渔民信息增加而优于基准，并且在平均重建误差方面的表现相当。
Robots can be used to collect environmental data in regions that are difficult for humans to traverse. However, limitations remain in the size of region that a robot can directly observe per unit time. We introduce a method for selecting a limited number of observation points in a large region, from which we can predict the state of unobserved points in the region. We combine a low rank model of a target attribute with an information-maximizing path planner to predict the state of the attribute throughout a region. Our approach is agnostic to the choice of target attribute and robot monitoring platform. We evaluate our method in simulation on two real-world environment datasets, each containing observations from one to two million possible sampling locations. We compare against a random sampler and four variations of a baseline sampler from the ecology literature. Our method outperforms the baselines in terms of average Fisher information gain per samples taken and performs comparably for average reconstruction error in most trials.
