Multiagent Persistent Monitoring of Targets With Uncertain States

We address the problem of persistent monitoring, where a finite set of mobile agents has to persistently visit a finite set of targets. Each of these targets has an internal state that evolves with linear stochastic dynamics. The agents can observe these states, and the observation quality is a function of the distance between the agent and a given target. The goal is then to minimize the mean squared estimation error of these target states. We approach the problem from an infinite horizon perspective, where we prove that, under some natural assumptions, the covariance matrix of each target converges to a limit cycle. The goal, therefore, becomes to minimize the steady-state uncertainty. Assuming that the trajectory is parameterized, we provide tools for computing the steady-state cost gradient. We show that, in 1-D (one dimensional) environments with bounded control and nonoverlapping targets, when an optimal control exists it can be represented using a finite number of parameters. We also propose an efficient parameterization of the agent trajectories for multidimensional settings using Fourier curves. Simulation results show the efficacy of the proposed technique in 1-D, 2-D, and 3-D scenarios.

Automated summary

The study focuses on persistent monitoring where mobile agents visit targets to minimize mean squared estimation error. It proves that under infinite horizon, the covariance matrix of targets converges to a limit cycle, and proposes using Fourier curves to parameterize agent trajectories.