This paper presents two algorithms for multi-agent dynamic coverage in spatiotemporal environments, where the coverage algorithms are informed by the method of data assimilation. In particular, we show that by considering the information assimilation algorithm, here a Numerical Gaussian Process Kalman Filter, the influence of measurements taken at one position on the uncertainty of the estimate at another location can be computed. We use this relationship to propose new coverage algorithms. Furthermore, we show that the controllers naturally extend to the multi-agent context, allowing for a distributed-control central-information paradigm for multi-agent coverage. Finally, we demonstrate the algorithms through a realistic simulation of a team of UAVs collecting wind data over a region in Austria.

翻译：本文提出了两种面向时空环境的多智能体动态覆盖算法，该算法融合了数据同化方法。具体而言，我们证明通过考虑信息同化算法（此处采用数值高斯过程卡尔曼滤波器），某一位置观测数据对另一位置估计不确定性的影响可被量化计算。利用这种关联性，我们提出了新颖的覆盖算法。进一步研究表明，该控制器可自然推广至多智能体场景，形成了分布式控制-集中式信息的多智能体覆盖范式。最后，我们通过在奥地利某区域的多无人机编队风场数据采集仿真实验，验证了所提算法的有效性。