1 research outputs found
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
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.Comment: 21 pages, 4 figure