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 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 multi-robot IPP. The proposed approach is demonstrated to be fast and accurate on real-world data.

翻译：本文针对环境监测中的多机器人信息路径规划（IPP）问题展开研究。该问题旨在确定环境中应被机器人访问的信息量最丰富的区域，以获取对环境的最大信息量。我们提出一种基于稀疏高斯过程的高效方法，通过梯度下降法在连续环境中优化路径。该方法能高效扩展至空间相关及时空相关环境。此外，本方法在满足路径约束（如距离预算、机器人速度和加速度限制）的同时，能同步优化信息路径。本方法适用于采用离散与连续感知型机器人、点状与非点状视野形状的IPP场景，并支持多机器人协同。实验结果表明，该方法在实际数据上具有快速性与准确性。