DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning

TL;DR

DyPNIPP is a novel RL-based informative path planning framework that predicts environment dynamics and uses domain randomization to enhance robustness in spatio-temporal environments, demonstrated effectively in wildfire scenarios.

Contribution

It introduces a dynamics prediction model and domain randomization into RL-based IPP to handle environment variability, improving robustness and adaptability.

Findings

Outperforms existing RL-based IPP methods in wildfire environments.

Enhances robustness across diverse spatio-temporal conditions.

Demonstrates significant improvements in environment adaptability.

Abstract

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.