Drones are vital for urban emergency search and rescue (SAR) due to the challenges of navigating dynamic environments with obstacles like buildings and wind. This paper presents a method that combines multi-objective reinforcement learning (MORL) with a convolutional autoencoder to improve drone navigation in urban SAR. The approach uses MORL to achieve multiple goals and the autoencoder for cost-effective wind simulations. By utilizing imagery data of urban layouts, the drone can autonomously make navigation decisions, optimize paths, and counteract wind effects without traditional sensors. Tested on a New York City model, this method enhances drone SAR operations in complex urban settings.

翻译：无人机在执行城市紧急搜索与救援(Search and Rescue, SAR)任务时，因需应对建筑物及风力等障碍物构成的动态环境挑战而至关重要。本文提出一种将多目标强化学习(Multi-Objective Reinforcement Learning, MORL)与卷积自编码器相结合的方法，以提升城市SAR中无人机的导航性能。该方法利用MORL实现多目标优化，并通过自编码器进行经济高效的风场模拟。通过利用城市布局的影像数据，无人机无需传统传感器即可自主做出导航决策、优化路径并抵消风力影响。在纽约市模型上的测试表明，该方法提升了无人机在复杂城市环境中执行SAR任务的能力。