Reinforcement Learning Based Escape Route Generation in Low Visibility Environments
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
Structure fires are responsible for the majority of fire-related deaths nationwide. In order to assist with the rapid evacuation of trapped people, this paper proposes the use of a system that determines optimal search paths for firefighters and exit paths for civilians in real time based on environmental measurements. Through the use of a LiDAR mapping system evaluated and verified by a trust range derived from sonar and smoke concentration data, a proposed solution to low visibility mapping is tested. These independent point clouds are then used to create distinct maps, which are merged through the use of a RANSAC based alignment methodology and simplified into a visibility graph. Temperature and humidity data are then used to label each node with a danger score, creating an environment tensor. After demonstrating how a Linear Function Approximation based Natural Policy Gradient RL methodology outperforms more complex competitors with respect to robustness and speed, this paper outlines two systems (savior and refugee) that process the environment tensor to create safe rescue and escape routes, respectively.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 11 references that are in the map. First posted on arXiv 27 May 2024.
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References · 1
- 2017Proximal Policy Optimization Algorithmscited by 15,215
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- 2025Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles
- 2024PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments
- 2024Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement
- 2026FIRE-VLM: A Vision-Language-Driven Reinforcement Learning Framework for UAV Wildfire Tracking in a Physics-Grounded Fire Digital Twin
- 2024EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Threat-Aware Visibility Estimation using Offline Reinforcement Learning
- 2026Beyond Specialization: Robust Reinforcement Learning Navigation via Procedural Map Generators
- 2026Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response
- 2024DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning