A Deep Q-learning based Path Planning and Navigation System for Firefighting Environments

Manish Bhattarai, Manish Bhattarai, Manel Martínez-Ramón

Abstract

Live fire creates a dynamic, rapidly changing environment that presents a worthy challenge for deep learning and artificial intelligence methodologies to assist firefighters with scene comprehension in maintaining their situational awareness, tracking and relay of important features necessary for key decisions as they tackle these catastrophic events. We propose a deep Q-learning based agent who is immune to stress induced disorientation and anxiety and thus able to make clear decisions for firefighter navigation based on the observed and stored facts in live fire environments. As a proof of concept, we imitate structural fire in a gaming engine called Unreal Engine which enables the interaction of the agent with the environment. The agent is trained with a deep Q-learning algorithm based on a set of rewards and penalties as per its actions on the environment. We exploit experience replay to accelerate the learning process and augment the learning of the agent with human-derived experiences. The agent trained under this deep Q-learning approach outperforms agents trained through alternative path planning systems and demonstrates this methodology as a promising foundation on which to build a path planning navigation assistant. This assistant is capable of safely guiding firefighters through live-fire environments in fireground navigation activities that range from exploration to personnel rescue.

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Paper Citation


in Harvard Style

Bhattarai M. and Martínez-Ramón M. (2021). A Deep Q-learning based Path Planning and Navigation System for Firefighting Environments.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 267-277. DOI: 10.5220/0010267102670277


in Bibtex Style

@conference{icaart21,
author={Manish Bhattarai and Manel Martínez-Ramón},
title={A Deep Q-learning based Path Planning and Navigation System for Firefighting Environments},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={267-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010267102670277},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Deep Q-learning based Path Planning and Navigation System for Firefighting Environments
SN - 978-989-758-484-8
AU - Bhattarai M.
AU - Martínez-Ramón M.
PY - 2021
SP - 267
EP - 277
DO - 10.5220/0010267102670277