Informative path planning (IPP) is an important planning paradigm for various real-world robotic applications such as environment monitoring. IPP involves planning a path that can learn an accurate belief of the quantity of interest, while adhering to planning constraints. Traditional IPP methods typically require high computation time during execution, giving rise to reinforcement learning (RL) based IPP methods. However, the existing RL-based methods do not consider spatio-temporal environments which involve their own challenges due to variations in environment characteristics. In this paper, we propose DyPNIPP, a robust RL-based IPP framework, designed to operate effectively across spatio-temporal environments with varying dynamics. To achieve this, DyPNIPP incorporates domain randomization to train the agent across diverse environments and introduces a dynamics prediction model to capture and adapt the agent actions to specific environment dynamics. Our extensive experiments in a wildfire environment demonstrate that DyPNIPP outperforms existing RL-based IPP algorithms by significantly improving robustness and performing across diverse environment conditions.

本研究针对传统信息路径规划方法在动态环境中计算时间长的问题，提出了DyPNIPP，一个鲁棒的基于强化学习的信息路径规划框架。该框架通过领域随机化训练代理，并引入动态预测模型，有效适应空间-时间环境的变化，实验显示DyPNIPP在火灾环境中显著改善了算法的鲁棒性和性能。

DyPNIPP：基于强化学习的鲁棒信息路径规划的环境动态预测