A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments

TL;DR

This paper presents a deep Q-learning based system for autonomous path planning and navigation in fire environments, demonstrating improved performance in simulated scenarios and aiding firefighter safety.

Contribution

It introduces a deep Q-learning agent for firefighting navigation that is resilient to stress and disorientation, trained using experience replay and human experiences.

Findings

Agent outperforms alternative path planning methods

Demonstrates effective navigation in simulated fire environments

Provides a foundation for firefighter assistance systems

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 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 capable of safely guiding fire fighters through live fire environments.