Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization

• Main Authors: Thomas Wiedemann, Cosmin Vlaicu, Josip Josifovski, Alberto Viseras
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Gas source localization; information gathering; reinforcement learning; mobile robot; deep learning
• Online Access: https://ieeexplore.ieee.org/document/9326418/

Gas source localization tackles the problem of finding leakages of hazardous substances such as poisonous gases or radiation in the event of a disaster. In order to avoid threats for human operators, autonomous robots dispatched for localizing potential gas sources are preferable. This work investigates a Reinforcement Learning framework that allows a robotic agent to learn how to localize gas sources. We propose a solution that assists Reinforcement Learning with existing domain knowledge based on a model of the gas dispersion process. In particular, we incorporate a priori domain knowledge by designing appropriate rewards and observation inputs for the Reinforcement Learning algorithm. We show that a robot trained with our proposed method outperforms state-of-the-art gas source localization strategies, as well as robots that are trained without additional domain knowledge. Furthermore, the framework developed in this work can also be generalized to a large variety of information gathering tasks.