低能见度环境中基于强化学习的逃生路线生成
Reinforcement Learning Based Escape Route Generation in Low Visibility Environments
AI总结：
针对建筑火灾低能见度疏散问题，论文融合LiDAR、声纳与烟雾数据建图并生成带危险分数的环境张量，利用线性函数逼近NPG强化学习方法实时生成救援与逃生路线。
摘要：
建筑火灾是全国大多数火灾相关死亡的原因。为协助被困人员快速疏散，本文提出使用一种系统，根据环境测量结果实时确定消防员的最佳搜索路径和平民的出口路径。通过使用由声纳和烟雾浓度数据得出的信任范围进行评估和验证的LiDAR建图系统，本文测试了一种针对低能见度建图的拟议解决方案。这些独立点云随后用于创建不同地图，并通过基于RANSAC的对齐方法进行合并，再简化为可见性图。温度和湿度数据随后用于为每个节点标注危险分数，从而创建环境张量。在证明基于线性函数逼近的自然策略梯度强化学习方法在鲁棒性和速度方面优于更复杂的竞争者之后，本文概述了两个系统（savior和refugee），它们处理环境张量，分别创建安全救援路线和逃生路线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
