Argus: Smartphone-Enabled Human Cooperation via Multi-agent Reinforcement Learning for Disaster Situational Awareness
Argus: Smartphone-Enabled Human Cooperation via Multi-agent Reinforcement Learning for Disaster Situational Awareness
复制标题
Argus：通过多智能体强化学习实现智能手机支持的人类合作以实现灾难态势感知
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Vincent Sritapan
中科院分区：
文献类型：
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作者：
Vidyasagar Sadhu;Gabriel Salles;D. Pompili;S. Zonouz;Vincent Sritapan
Argus利用多智能体强化学习(MAIL)框架，使用事故区域周围的智能体创建灾难场景的3D地图，以促进救援行动。特工既可以是灾难现场的人类旁观者，也可以是可以帮助人类的无人机或机器人。这些特工按照Marl算法的指示，使用他们的智能手机(如果是无人机，则使用机载摄像头)捕获现场图像。这些图像被用来实时构建灾难现场的3D地图。通过仿真和真实实验，对该框架在跟踪环境随机动态方面的有效性进行了评估。
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.