我们研究多智能体覆盖算法在城市环境中的自主监测和巡逻。我们考虑的情况下，一队飞行代理使用向下面对的摄像头（或类似的传感器），以观察在街道上的建筑物外的环境。建筑物被认为是阻碍移动的障碍物，并且假设相机在最大高度以上无效。我们研究了与此场景相关的多智能体城市覆盖问题，包括：（1）静态多智能体城市覆盖，其中智能体被期望从静态位置观察环境，以及（2）动态多智能体城市覆盖，其中智能体连续移动通过环境。我们实验评估六种不同的多智能体覆盖方法，包括：三种类型的遍历覆盖（以不同的方式避免建筑物），割草机扫描，基于Voronoi区域的控制，和一个天真的网格方法。我们评估了所有算法的四个性能指标（覆盖率，修订计数，修订时间，以及随着时间的推移查看的区域的积分），在四种类型的城市环境[低密度，高密度] x [矮建筑物，高层建筑物]，团队规模从2到25代理。我们相信这是第一次在城市环境中对这些方法进行广泛的比较。我们的研究结果突出了静态和动态方法的相对性能如何根据团队规模与搜索区域的比率而变化，以及城市环境的不同特征（高，矮，密集，稀疏，混合）对每个算法的相对影响。
英文摘要：
We study multi-agent coverage algorithms for autonomous monitoring and patrol in urban environments. We consider scenarios in which a team of flying agents uses downward facing cameras (or similar sensors) to observe the environment outside of buildings at street-level. Buildings are considered obstacles that impede movement, and cameras are assumed to be ineffective above a maximum altitude. We study multi-agent urban coverage problems related to this scenario, including: (1) static multi-agent urban coverage, in which agents are expected to observe the environment from static locations, and (2) dynamic multi-agent urban coverage where agents move continuously through the environment. We experimentally evaluate six different multi-agent coverage methods, including: three types of ergodic coverage (that avoid buildings in different ways), lawn-mower sweep, voronoi region based control, and a naive grid method. We evaluate all algorithms with respect to four performance metrics (percent coverage, revist count, revist time, and the integral of area viewed over time), across four types of urban environments [low density, high density] x [short buildings, tall buildings], and for team sizes ranging from 2 to 25 agents. We believe this is the first extensive comparison of these methods in an urban setting. Our results highlight how the relative performance of static and dynamic methods changes based on the ratio of team size to search area, as well the relative effects that different characteristics of urban environments (tall, short, dense, sparse, mixed) have on each algorithm.
Multi-Agent gatekeeper: Safe Flight Planning and Formation Control for Urban Air Mobility
Department of Electrical & Computer Engineering（电气与计算机工程系）；Department of Robotics（机器人学系）；Department of Robotics and Department of Aerospace Engineering（机器人学系和航空航天工程系）
Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments
Institute of Automation, Chinese Academy of Science, China（中国科学院自动化研究所）；Beijing Institute of Technology, China（北京理工大学）；Institute of Automation, Chinese Academy of Sciences, China（中国科学院自动化研究所）；Tsinghua University, China（清华大学）
