提出动态环境下的鲁棒路径规划方法，提升机器人在变化环境中的感知效率。
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning
- 通过域随机化与动态预测模型，让智能体适应不同环境变化
- 在野火环境中显著优于现有RL路径规划算法
- 适合需要应对复杂动态环境的机器人感知任务
信息路径规划（IPP）是环境监测等实际机器人应用中的重要规划范式，旨在规划一条能准确学习感兴趣量信念的路径，同时满足约束条件。传统方法执行时计算开销高，因此出现基于强化学习（RL）的IPP方法。然而，现有方法未考虑时空变化环境带来的挑战。本文提出DyPNIPP，一种针对具有变化动态的时空环境的鲁棒性RL-based IPP框架。通过域随机化训练智能体以覆盖多样环境，并引入动态预测模型捕捉并适配特定环境动态。大量实验在野火环境中表明，DyPNIPP显著提升了鲁棒性，在多种环境条件下表现更优。
原文摘要 · Abstract (English)
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.
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