We address the problem of maintaining resource availability in a networked multirobot team performing distributed tracking of an unknown number of targets in a bounded environment. Robots are equipped with sensing and computational resources, enabling them to cooperatively track a set of targets 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, the 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 monitor executes the network reconfiguration computations. We consider two different PHD fusion methods and propose four different mixed-integer semi-definite programming (MISDP) formulations (two formulations for each PHD fusion method) to accomplish our objective. All MISDP formulations are validated in simulation.
Resilience in Multirobot Multitarget Tracking With Unknown Number of Targets Through Reconfiguration
R. Ramachandran,Nicole Fronda,G. Sukhatme
Published 2020 in IEEE Transactions on Control of Network Systems
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- Publication year
2020
- Venue
IEEE Transactions on Control of Network Systems
- Publication date
2020-04-01
- Fields of study
Computer Science, Engineering
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