Understanding Fire Through Thermal Radiation Fields for Mobile Robots
This work addresses the need for autonomous navigation in fire-affected environments for disaster response robots, but validation is limited to controlled settings without quantitative performance metrics.
The authors present a method for mobile robots to understand fire by building real-time thermal radiation fields from depth and thermal images, enabling thermally safe navigation. They validate on a Spot robot in controlled settings, showing the robot can avoid hazards while reaching goals.
Safely moving through environments affected by fire is a critical capability for autonomous mobile robots deployed in disaster response. In this work, we present a novel approach for mobile robots to understand fire through building real-time thermal radiation fields. We register depth and thermal images to obtain a 3D point cloud annotated with temperature values. From these data, we identify fires and use the Stefan-Boltzmann law to approximate the thermal radiation in empty spaces. This enables the construction of a continuous thermal radiation field over the environment. We show that this representation can be used for robot navigation, where we embed thermal constraints into the cost map to compute collision-free and thermally safe paths. We validate our approach on a Boston Dynamics Spot robot in controlled experimental settings. Our experiments demonstrate the robot's ability to avoid hazardous regions while still reaching navigation goals. Our approach paves the way toward mobile robots that can be autonomously deployed in fire-affected environments, with potential applications in search-and-rescue, firefighting, and hazardous material response.