Information-Driven Path Planning

Abstract

The era of robotics-based environmental monitoring has given rise to many interesting areas of research. A key challenge is that robotic platforms and their operations are typically constrained in ways that limit their energy, time, or travel distance, which in turn limits the number of measurements that can be collected. Therefore, paths need to be planned to maximize the information gathered about an unknown environment while satisfying the given budget constraint, which is known as the informative planning problem. This review discusses the literature dedicated to information-driven path planning, introducing the key algorithmic building blocks as well as the outstanding challenges. Machine learning approaches have been introduced to solve the information-driven path planning problem, improving both efficiency and robustness. This review started with the fundamental building blocks of informative planning for environment modeling and monitoring, followed by integration with machine learning, emphasizing how machine learning can be used to improve the robustness and efficiency of informative path planning in robotics.