This paper addresses multi-robot informative path planning (IPP) for environmental monitoring. The problem involves determining informative regions in the environment that should be visited by robots in order to gather the most amount of information about the environment. We propose an efficient sparse Gaussian process-based approach that uses gradient descent to optimize paths in continuous environments. Our approach efficiently scales to both spatially and spatio-temporally correlated environments. Moreover, our approach can simultaneously optimize the informative paths while accounting for routing constraints, such as a distance budget and limits on the robot's velocity and acceleration. Our approach can be used for IPP with both discrete and continuous sensing robots, with point and non-point field-of-view sensing shapes, and for both single and multi-robot IPP. We demonstrate that the proposed approach is fast and accurate on real-world data.

翻译：本文研究了环境监测中的多机器人信息路径规划（IPP）问题。该问题旨在确定环境中应被机器人访问的信息丰富区域，以收集关于环境的最大量信息。我们提出了一种基于稀疏高斯过程的高效方法，该方法利用梯度下降法在连续环境中优化路径。我们的方法能够高效地扩展到空间相关及时空相关环境。此外，该方法可在优化信息路径的同时考虑路径约束，如距离预算及机器人速度与加速度限制。该方法适用于离散和连续感知机器人、点状与非点状视场感知形状，以及单机器人与多机器人信息路径规划。通过在真实数据上的实验，我们证明了所提方法具有快速且准确的优势。