Distributed Persistent Monitoring of Moving Targets Using High Order Control Barrier Functions

This Thesis considers the problem of persistently monitoring a set of moving targets using a team of aerial vehicles. Each agent of the network is assumed equipped with a camera providing bearing measurements with a limited FoV, and it implements an Information Consensus Filter (ICF) to estimate the state of the target(s). It is shown that, thanks to a suitable choice of the output equation, the filter can be proven to be globally exponentially convergent under Persistency of Excitation (PE) conditions. This is then leveraged for proposing a distributed control scheme that allows maintaining a prescribed minimum PE level for ensuring filter convergence. At the same time, the agents in the group are also allowed to perform additional tasks of interest while maintaining a collective observability of the target(s). In order to enforce satisfaction of the observability constraint, two main tools are exploited: the weighted Observability Gramian with a forgetting factor as a measure of the cumulative acquired information, and the use of High Order Control Barrier Functions (HOCBFs) as a mean to maintain a minimum level of observability for the targets. Simulation results are reported to prove the effectiveness of this approach.