Understanding Fire Through Thermal Radiation Fields for Mobile Robots
- URL: http://arxiv.org/abs/2602.19108v1
- Date: Sun, 22 Feb 2026 09:44:12 GMT
- Title: Understanding Fire Through Thermal Radiation Fields for Mobile Robots
- Abstract summary: Safely moving through environments affected by fire is a critical capability for autonomous mobile robots deployed in disaster response.<n>We present a novel approach for mobile robots to understand fire through building real-time thermal radiation fields.<n>Our approach paves the way toward mobile robots that can be autonomously deployed in fire-affected environments.
- Score: 14.308312017562756
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
Related papers
- SafeGuard ASF: SR Agentic Humanoid Robot System for Autonomous Industrial Safety [0.3499870393443268]
We present SafeGuard ASF, a comprehensive framework deploying humanoid robots for autonomous hazard detection in industrial environments.<n>Our system integrates multi-modal perception (RGB-D imaging), a ReAct-based agentic reasoning framework, and learned locomotion policies on the Unitree G1 humanoid platform.<n>We validate our system in both simulation and real-world environments, demonstrating autonomous patrol, human detection with visual perception, and obstacle avoidance capabilities.
arXiv Detail & Related papers (2026-03-26T11:58:44Z) - Fire as a Service: Augmenting Robot Simulators with Thermally and Visually Accurate Fire Dynamics [13.663004460895912]
Fire as a Service is a novel co-simulation framework that augments existing robot simulators with high-fidelity and efficient fire simulations.<n>Our framework can be integrated with diverse robot simulators to generate physically accurate fire behavior, benchmark thermal hazards encountered by robotic platforms, and collect realistic multimodal data.<n>By adding fire dynamics to robot simulations, F provides a scalable pathway toward safer, more reliable deployment of robots in fire scenarios.
arXiv Detail & Related papers (2026-03-19T15:55:02Z) - ThermalDiffusion: Visual-to-Thermal Image-to-Image Translation for Autonomous Navigation [6.524847658755803]
We propose a solution to augment multi-modal datasets with synthetic thermal data to enable widespread and rapid adaptation of thermal cameras.<n>We explore the use of conditional diffusion models to convert existing RGB images to thermal images using self-attention to learn the thermal properties of real-world objects.
arXiv Detail & Related papers (2025-06-26T03:18:22Z) - Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities [65.98704516122228]
The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments.<n>This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments.<n>We present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions.
arXiv Detail & Related papers (2025-05-14T15:28:43Z) - Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy [53.048430683355804]
Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks.<n>We present Safety-as-policy, which includes (i) a world model to automatically generate scenarios containing safety risks and conduct virtual interactions, and (ii) a mental model to infer consequences with reflections.<n>We show that Safety-as-policy can avoid risks and efficiently complete tasks in both synthetic dataset and real-world experiments.
arXiv Detail & Related papers (2024-11-27T12:27:50Z) - Generalizable Humanoid Manipulation with 3D Diffusion Policies [66.78220965526732]
We build a real-world robotic system to address the problem of autonomous manipulation by humanoid robots.<n>Our system is mainly an integration of 1) a whole-upper-body robotic teleoperation system to acquire human-like robot data, and 2) a 25-DoF humanoid robot platform with a height-adjustable cart and a 3D LiDAR sensor.<n>We show that using only data collected in one scene and with only onboard computing, a full-sized humanoid robot can autonomously perform skills in diverse real-world scenarios.
arXiv Detail & Related papers (2024-10-14T17:59:00Z) - Robot Learning with Sensorimotor Pre-training [98.7755895548928]
We present a self-supervised sensorimotor pre-training approach for robotics.
Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens.
We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
arXiv Detail & Related papers (2023-06-16T17:58:10Z) - COPILOT: Human-Environment Collision Prediction and Localization from
Egocentric Videos [62.34712951567793]
The ability to forecast human-environment collisions from egocentric observations is vital to enable collision avoidance in applications such as VR, AR, and wearable assistive robotics.
We introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras.
We propose a transformer-based model called COPILOT to perform collision prediction and localization simultaneously.
arXiv Detail & Related papers (2022-10-04T17:49:23Z) - Dense Crowd Flow-Informed Path Planning [24.849908664615104]
Flow-field extraction and discrete search are used to create Flow-Informed path planning.
A robot using FIPP was able not only to reach its goal more quickly but also was shown to be more socially compliant than a robot using traditional techniques.
arXiv Detail & Related papers (2022-06-01T18:40:57Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Development of a conversing and body temperature scanning autonomously
navigating robot to help screen for COVID-19 [0.0]
The goal is to develop a functioning solution that performs the above tasks.
An autonomously navigating mobile robot is used with a manipulator controlled using a face tracking algorithm.
recommendations will be made for enhancements that could be incorporated when approaching commercialization.
arXiv Detail & Related papers (2021-06-18T03:30:11Z) - Deep Reinforcement learning for real autonomous mobile robot navigation
in indoor environments [0.0]
We present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner.
The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the goal.
The output actions of an Asynchronous Advantage Actor-Critic network (GA3C) are the linear and angular velocities for the robot.
arXiv Detail & Related papers (2020-05-28T09:15:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.