In this paper, we investigate the problem of decomposing 2D environments for robot coverage planning. Coverage path planning (CPP) involves computing a cost-minimizing path for a robot equipped with a coverage or sensing tool so that the tool visits all points in the environment. CPP is an NP-Hard problem, so existing approaches simplify the problem by decomposing the environment into the minimum number of sectors. Sectors are sub-regions of the environment that can each be covered using a lawnmower path (i.e., along parallel straight-line paths) oriented at an angle. However, traditional methods either limit the coverage orientations to be axis-parallel (horizontal/vertical) or provide no guarantees on the number of sectors in the decomposition. We introduce an approach to decompose the environment into possibly overlapping rectangular sectors. We provide an approximation guarantee on the number of sectors computed using our approach for a given environment. We do this by leveraging the submodular property of the sector coverage function, which enables us to formulate the decomposition problem as a submodular set cover (SSC) problem with well-known approximation guarantees for the greedy algorithm. Our approach improves upon existing coverage planning methods, as demonstrated through an evaluation using maps of complex real-world environments.
MSTC*:Multi-robot Coverage Path Planning under Physical Constraints
Shanghai Key Laboratory of Trustworthy Computing（上海市可信计算重点实验室）；Engineering Research Center of Software/Hardware Co-design Technology and Application (MoE)（软硬件协同设计技术与应用教育部工程研究中心）；School of Software Engineering, East China Normal University（华东师范大学软件工程学院）
Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures
A*STAR Institute of Information Research (IR)（新加坡科技研究局信息研究所）；A*STAR Institute of High Performance Computing (IHPC)（新加坡科技研究局高性能计算研究所）；A*STAR Initiative for Artificial Intelligence and Analytics (AI)（新加坡科技研究局人工智能与分析计划）；Department of Mechanical Engineering, Carnegie Mellon University（卡内基梅隆大学机械工程系）
TRACE: Coverage Path Planning for Unknown Environments Using Hierarchical Coverage Tree
Jinan University（暨南大学）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；Wuyi University（五邑大学）；South China University of Technology（华南理工大学）
Coverage Path Planning for Autonomous Sailboats in Inhomogeneous and Time-Varying Oceans: A Spatiotemporal Optimization Approach
Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）；Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院海洋工程研究所）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学 Hangzhou 全球科技创新中心）；Department of Engineering, Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学工程系）；School of Ocean and Civil Engineering, Shanghai Jiao Tong University（上海交通大学海洋与Civil Engineering学院）
Adaptive Coverage Path Planning for Efficient Exploration of Unknown Environments
California Institute of Technology（加州理工学院）；Stanford University（斯坦福大学）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；University of California Los Angeles（加州大学洛杉矶分校）
MDCPP: Multi-Robot Dynamic Coverage Path Planning for Workload Adaptation
School of Electrical and Automation Engineering, Nanjing Normal University（南京师范大学电气与自动化工程学院）；Computer, Electrical, and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology（王国 Abdullah 科学与技术大学计算机、电气和数学科学与工程系）
Brazilian Institute of Robotics（巴西机器人研究所）；SENAI CIMATEC（巴西国家工业培训服务西玛特克分校）；Department of Informatics, University of Oslo（奥斯陆大学信息学系）；SENAI Institute for Innovation in Automation（巴西国家工业培训服务自动化创新研究所）；Robotics Innovation Center, DFKI GmbH（德国人工智能研究中心机器人创新中心）
Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions
College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）；Department of Electrical and Computer Engineering, University of Connecticut（康涅狄格大学电气与计算机工程系）；Global College, Shanghai Jiao Tong University（上海交通大学密西根学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Electronics and Information Engineering, Wuyi University（五邑大学电子与信息工程学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
Instituto Politécnico Nacional (IPN)（墨西哥国立理工学院）；Centro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC)（计算技术创新与发展中心）；Instituto Politécnico Nacional（墨西哥国立理工学院）；UPIICSA（高等工程、设计与技术学院）
Efficient Probabilistic Planning with Maximum-Coverage Distributionally Robust Backward Reachable Trees
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology（信息与决策系统实验室，麻省理工学院）；Draper, Cambridge MA 02139（Draper，剑桥MA 02139）
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
TUM School of Engineering and Design, Technical University of Munich（慕尼黑技术大学工程与设计学院）；Dept. of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）
