Abstract
Argus exploits a Multi-Agent Reinforcement Learning (MARL) framework to create a 3D mapping of the disaster scene using agents present around the incident zone to facilitate the rescue operations. The agents can be both human bystanders at the disaster scene as well as drones or robots that can assist the humans. The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm. These images are used to build real time a 3D map of the disaster scene. Via both simulations and real experiments, an evaluation of the framework in terms of effectiveness in tracking random dynamicity of the environment is presented.
Abstract (translated)
Argus利用多代理强化学习（multi-agent-reinformation learning，MARL）框架，使用事件区域周围的代理创建灾难场景的3D映射，以促进救援行动。这些特工既可以是灾难现场的旁观者，也可以是无人机或机器人来帮助人类。特工们参与了按照marl算法的指示，使用他们的智能手机（或车载摄像头，以防无人驾驶飞机）捕捉现场的图像。这些图像用于实时构建灾难场景的3D地图。通过仿真和实际实验，对该框架在跟踪环境随机动态性方面的有效性进行了评价。
URL
https://arxiv.org/abs/1906.03037
