This paper presents a method for Bayesian multi-robot peer-to-peer data fusion where any pair of autonomous robots hold non-identical, but overlapping parts of a global joint probability distribution, representing real world inference tasks (e.g., mapping, tracking). It is shown that in dynamic stochastic systems, filtering, which corresponds to marginalization of past variables, results in direct and hidden dependencies between variables not mutually monitored by the robots, which might lead to an overconfident fused estimate. The paper makes both theoretical and practical contributions by providing (i) a rigorous analysis of the origin of the dependencies and and (ii) a conservative filtering algorithm for heterogeneous data fusion in dynamic systems that can be integrated with existing fusion algorithms. This work uses factor graphs as an analysis tool and an inference engine. Each robot in the network maintains a local factor graph and communicates only relevant parts of it (a sub-graph) to its neighboring robot. We discuss the applicability to various multi-robot robotic applications and demonstrate the performance using a multi-robot multi-target tracking simulation, showing that the proposed algorithm produces conservative estimates at each robot.
Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation
Institute for Intelligent Cooperating Systems, Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学智能协作系统研究所）；Smead Aerospace Engineering Sciences Department, University of Colorado Boulder（科罗拉多大学博尔德分校斯米德航空航天工程科学系）
MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
Center for Cyber-Physical Systems, Indian Institute of Science（印度科学研究院中心 for 联合体系统）；Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）；Department of Aeronautics and Astronautics, Stanford University（斯坦福大学航空与航天工程系）
