We address the problem of maintaining resource availability in a networked multi-robot team performing distributed tracking of unknown number of targets in an environment of interest. Based on our model, robots are equipped with sensing and computational resources enabling them to cooperatively track a set of targets in an environment using a distributed Probability Hypothesis Density (PHD) filter. We use the trace of a robot's sensor measurement noise covariance matrix to quantify its sensing quality. While executing the tracking task, if a robot experiences sensor quality degradation, then robot team's communication network is reconfigured such that the robot with the faulty sensor may share information with other robots to improve the team's target tracking ability without enforcing a large change in the number of active communication links. A central system which monitors the team executes all the network reconfiguration computations. We consider two different PHD fusion methods in this paper and propose four different Mixed Integer Semi-Definite Programming (MISDP) formulations (two formulations for each PHD fusion method) to accomplish our objective. All four MISDP formulations are validated in simulation.
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
University of Cambridge（剑桥大学）；University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Southern California（南加利福尼亚大学）；Amazon（亚马逊）；Army Research Laboratory（陆军研究实验室）
Resilient Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Department of Electrical and Computer Engineering, Drexel University（电子与计算机工程系，德雷塞尔大学）；GRASP Lab, University of Pennsylvania（GRASP实验室，宾夕法尼亚大学）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（机器人与智能机器研究所，佐治亚理工学院）；Department of Computer Science, University of Southern California（计算机科学系，南加州大学）
