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

环境场估计系统非常有趣，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，固定式传感器被安装用于此类任务。然而，一组自主水下航行器（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传感器网络可以实现出色的性能，并且已经进行了深入的研究，但在水下场景中，由于通信信道不可靠且带宽低，车辆无法定期与中央单元通信，分散的方法通常更受青睐。真正分散式多auv系统的发展需要超越当前技术水平的扩展。在本研究项目中考虑了一组部署到海洋环境中执行分布式时空估计任务的auv。auv对动态物理过程进行测量，这些物理过程被建模为非平稳高斯随机场。这些场描述了诸如水温或污染物浓度等量的演变。基于同步和路径积分控制，利用高斯随机场的协方差对水下航行器群进行分散协调。基于同步和路径积分控制的方法使分散的环境探索成为可能。由于未知领域是通过非参数方法（高斯过程）建模的，因此需要最少的先验知识。最初未知的协方差函数可以沿场估计。该方法允许分析探索性问题的基本权衡，总结如下问题：是否应该收集未来的观测数据以改进现场模型（更好的协方差函数估计），还是应该在当前（可能不准确）模型显示最高不确定性的位置进行观测？力学与海洋工程研究所近年来在水下航行器动力学和控制的理论和实践方面进行了研究并发表了论文。采用基于物理的建模方法，推导了集中控制的水下航行器群探测环境场的算法。微型auv已经被开发出来。这些结果是开发一个真正分散的勘探控制框架的起点。并对场不确定性与分散协调控制相结合进行了初步研究。进一步的研究表明，车辆可以探索平流扩散场，其中场信度由高斯马尔可夫随机场表示。这个概念的验证将通过实验进行。利用开发的控制框架，一组微型auv将执行水平曲线跟踪，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.