Shekaramiz Mohammad, Moon Todd K, Gunther Jacob H
Electrical and Computer Engineering Department and Information Dynamics Laboratory, Utah State University, 4120 Old Main Hill, Logan, UT 84322-4120, USA.
Entropy (Basel). 2019 Jun 5;21(6):568. doi: 10.3390/e21060568.
We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.
我们研究多个移动传感器的部署，以探索未知区域，绘制包含诸如热量、电子密度等物理量浓度的区域。这种探索需要在两个需求之间进行权衡：一方面是在已知包含感兴趣物理量的区域继续采集数据，以细化测量结果；另一方面是在未观测区域采集数据，试图发现该物理量可能存在的新区域。做出合理且实际的决策以同时实现探索和数据细化这两个目标似乎既困难又相互矛盾。为此，我们提出了一个通用框架，该框架为移动传感器的轨迹做出有价值负载的决策。该框架采用高斯过程回归模型来预测未观测位置处感兴趣物理量的分布。然后，使用认知效用控制器对传感器的轨迹进行决策。提供了一个示例来说明所提出框架的优点和适用性。
