Decentralized Cooperative Exploration of Nonstationary Spatiotemporal Environmental Fields with Autonomous Underwater Vehicle Systems

用于环境场估计的系统引起了极大的兴趣，因为它们允许在海洋学研究中收集数据，定位污染源，并分析海上风电场的流场。通常，固定传感器被安装用于这样的任务。然而，一组自主水下机器人（AUV）可以实现更高的空间分辨率，由于可能的传感器重新定位。虽然由中央单元控制的移动的传感器网络可以实现很好的性能，并且已经进行了深入的研究，但分散式方法通常适用于水下场景，其中车辆无法定期与中央单元通信，因为通信信道不可靠且带宽较低。 研究了一组AUV部署在海洋环境中执行分布式时空估计任务的情况。 AUV测量动态物理过程，这些过程被建模为非平稳高斯随机场。这些字段描述了水温或污染物浓度等量的演变。 利用高斯随机场的协方差进行基于同步和路径积分控制的AUV群分散协调。基于同步和路径积分控制的方法使得分散的环境探索成为可能。由于未知领域的建模非参数的方法（高斯过程）的先验知识是必需的。最初未知的协方差函数可以沿场沿着估计。 该方法允许分析一个基本的权衡，在探索性的问题，这是总结了以下问题：未来的观测应收集，以改善现场模型（一个更好的协方差函数估计），或应采取的意见，在当前（可能不准确）的位置模型表示最高的不确定性？力学和海洋工程研究所近年来进行并发表了关于水下机器人动力学和控制的理论和实践方面的研究。 算法集中控制的AUV群体探索环境领域已经推导出与物理为基础的方法建模。微型水下机器人已经研制成功。这些结果是开发一个真正分散的控制框架的起点。此外，初步研究结合领域的不确定性与分散协调控制已经进行。进一步的研究表明，车辆可以探测对流扩散场，其中场信念由高斯马尔可夫随机场表示，这一概念将进行实验验证。利用开发的控制框架，一组微型AUV将执行诸如水平曲线跟踪、梯度爬升和源定位等任务。

Systems for environmental field estimation are of great interest because they allow data collection in oceanographic research, locating pollution sources, and analyzing flow fields at offshore wind farms. Usually, stationary sensors are installed for such tasks. However, a group of Autonomous Underwater Vehicles (AUVs) can achieve a higher spatial resolution due to possible sensor repositioning. While mobile sensor networks controlled by a central unit can achieve great performance and are already investigated in great depth, decentralized approaches are often favored in underwater scenarios where vehicles cannot communicate with a central unit regularly, because communication channels are unreliable and of low bandwidth. The development of truly decentralized multi-AUV systems requires extensions beyond the state of the art.A group of AUVs deployed into a marine environment performing distributed spatiotemporal estimation tasks is considered in this research project. The AUVs take measurements of dynamic physical processes which are modelled as non-stationary Gaussian random fields. The fields describe the evolution of quantities such as water temperature or concentration of pollutants. The covariance of the Gaussian random field is used for decentralized coordination of the AUV group based on synchronization and path integral control. An approach based on synchronization and path integral control enables decentralized environmental exploration. Since the unknown fields are modeled by a nonparametric approach (Gaussian processes) a minimum of a priori knowledge is required. The initially unknown covariance function can be estimated along the field. The approach allows analyzing a fundamental tradeoff in explorative problems which is summarized by the following question: Shall future observations be collected to improve the field model (a better covariance function estimate), or shall observations be taken at locations where the current (possibly inaccurate) model indicates the highest uncertainty?The Institute of Mechanics and Ocean Engineering has in recent years conducted and published research on both theoretical and practical aspects of AUV dynamics and controls. Algorithms for centrally controlled AUV groups for exploring environmental fields have been derived which are modeled with physics-based approaches. Micro AUVs have been developed. These results are the starting point for developing a truly decentralized controls framework for exploration. Furthermore, preliminary studies on combining field uncertainty with decentralized coordination control have been conducted. Further studies show that a vehicle can explore an advection-diffusion field where the field belief is represented by a Gaussian Markov random field.The verification of the concept will be conducted experimentally. With the developed controls framework, a group of micro-AUVs will perform tasks such as level curve tracking, gradient climbing and source localization.