We consider the problem of classifying a map using a team of communicating robots. It is assumed that all robots have localized visual sensing capabilities and can exchange their information with neighboring robots. Using a graph decomposition technique, we proposed an offline learning structure that makes every robot capable of communicating with and fusing information from its neighbors to plan its next move towards the most informative parts of the environment for map classification purposes. The main idea is to decompose a given undirected graph into a union of directed star graphs and train robots w.r.t a bounded number of star graphs. This will significantly reduce the computational cost of offline training and makes learning scalable (independent of the number of robots). Our approach is particularly useful for fast map classification in large environments using a large number of communicating robots. We validate the usefulness of our proposed methodology through extensive simulations.
Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression
Zhejiang University（浙江大学）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）；National 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）
Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations
MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering（麻省理工学院-伍兹霍尔海洋研究所联合海洋学/应用海洋科学与工程项目）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；Woods Hole Oceanographic Institution (WHOI)（伍兹霍尔海洋研究所）
UDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints
Department of Aerospace Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校航空航天工程系）；School of Intelligent Science and Technology, Nanjing University（南京大学智能科学与技术学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；NVIDIA Research（NVIDIA研究）；Department of Mechanical Engineering, University of California, Berkeley（加州大学伯克利分校机械工程系）
Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models
Georgia Institute of Technology（佐治亚理工学院）；Massachusetts Institute of Technology（麻省理工学院）；U.S. Army Research Laboratory（美国陆军研究实验室）；UC San Diego（加州大学圣地亚哥分校）
Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy
Singapore University of Technology and Design（新加坡科技设计大学）；Inria Grenoble, Grenoble Alpes University（法国国家信息与自动化研究所格勒诺布尔分部，格勒诺布尔阿尔卑斯大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Distributed control and navigation system for quadrotor UAVs in GPS-denied environments
Institute for Systems Analysis of Russian Academy of Sciences（俄罗斯科学院系统分析研究所）；Soloviev Rybinsk State Aviation Technical University（索洛维约夫雷宾斯克国立航空技术大学）；NPP SATEK plus（SATEK plus公司）
