A Generalized Voronoi Graph based Coverage Control Approach for Non-Convex Environment
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente（代尔夫特理工大学电子工程、数学和计算机科学学院）；Department of Electrical Engineering, Wright State University（怀特州立大学电子工程系）；College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（西安交通大学人机混合增强智能国家级重点实验室）
To address the challenge of efficient coverage by multi-robot systems in non-convex regions with multiple obstacles, this paper proposes a coverage control method based on the Generalized Voronoi Graph (GVG), which has two phases: Load-Balancing Algorithm phase and Collaborative Coverage phase. In Load-Balancing Algorithm phase, the non-convex region is partitioned into multiple sub-regions based on GVG. Besides, a weighted load-balancing algorithm is developed, which considers the quality differences among sub-regions. By iteratively optimizing the robot allocation ratio, the number of robots in each sub-region is matched with the sub-region quality to achieve load balance. In Collaborative Coverage phase, each robot is controlled by a new controller to effectively coverage the region. The convergence of the method is proved and its performance is evaluated through simulations.
Balanced Collaborative Exploration via Distributed Topological Graph Voronoi Partition
College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）；National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（西安交通大学人机混合增强智能国家级重点实验室）；National Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家级重点实验室）
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。
Energy-Efficient Multi-Robot Coverage Path Planning of Non-Convex Regions of Interests
Department of Earth and Environmental Sciences, University of New Orleans（地球与环境科学系，新奥尔良大学）；School of Computing and Information Sciences, Florida International University（计算与信息科学学院，佛罗里达国际大学）；Providence College, Department of Mathematics & Computer Science（普罗维登斯学院，数学与计算机科学系）
NavEX: A Multi-Agent Coverage in Non-Convex and Uneven Environments via Exemplar-Clustering
Department of Mechanical and Aerospace Engineering, University of California, Irvine（机械与航空航天工程系，加州大学伊维特分校）；U.S. DEVCOM Army Research Laboratory (ARL)（美国陆军研究实验室（ARL））
Domains as Objectives: Domain-Uncertainty-Aware Policy Optimization through Explicit Multi-Domain Convex Coverage Set Learning
Nara Institute of Science and Technology (NAIST)（奈良科学技术研究所）；National Institute of Informatics (NII)（情报学研究所）；The Graduate University for Advanced Studies (SOKENDAI)（高级研究大学院大学）
