Resilience in multi-robot multi-target tracking with unknown number of targets through reconfiguration

We address the problem of maintaining resource availability in a networked\nmulti-robot team performing distributed tracking of unknown number of targets\nin an environment of interest. Based on our model, robots are equipped with\nsensing and computational resources enabling them to cooperatively track a set\nof targets in an environment using a distributed Probability Hypothesis Density\n(PHD) filter. We use the trace of a robot's sensor measurement noise covariance\nmatrix to quantify its sensing quality. While executing the tracking task, if a\nrobot experiences sensor quality degradation, then robot team's communication\nnetwork is reconfigured such that the robot with the faulty sensor may share\ninformation with other robots to improve the team's target tracking ability\nwithout enforcing a large change in the number of active communication links. A\ncentral system which monitors the team executes all the network reconfiguration\ncomputations. We consider two different PHD fusion methods in this paper and\npropose four different Mixed Integer Semi-Definite Programming (MISDP)\nformulations (two formulations for each PHD fusion method) to accomplish our\nobjective. All four MISDP formulations are validated in simulation.\n