This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian Process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of interest. To spatially and temporally navigate the group of robots so that they can optimally acquire maximal information gains while their connectivity is preserved, we propose a novel multistep prediction informative path planning optimization strategy employing our newly defined local cost functions. By using the dual decomposition method, it is feasible and practical to effectively solve the optimization problem in a distributed manner. The proposed method was validated through synthetic experiments utilizing real-world data sets.

翻译：本文研究了移动机器人传感器网络在时空分布映射中的信息路径规划问题。机器人在移动过程中能够从感兴趣区域采集带噪声的测量值，以构建时空场的高斯过程模型。该模型随后用于预测不同感兴趣点处的时空现象。为在空间和时间上引导机器人群体在保持连接性的同时最优地获取最大信息增益，我们提出了一种基于新定义局部成本函数的多步预测信息路径规划优化策略。通过采用对偶分解方法，能够以分布式方式有效且实用地求解该优化问题。所提方法通过利用真实数据集的合成实验进行了验证。