Optimal Event-Driven Multiagent Persistent Monitoring of a Finite Set of Data Sources
Nan Run Zhou, Xi Yu, Sean B. Andersson, Christos G. Cassandras · IEEE Transactions on Automatic Control · 2018
We consider the problem of controlling the movement of multiple cooperating agents so as to minimize an uncertainty metric associated with a finite number of data sources. In a one-dimensional (1-D) mission space, we adopt an optimal control framework and show that the solution can be reduced to a simpler parametric optimization problem: Determining a sequence of locations where each agent may dwell for a finite amount of time and then switch direction. This amounts to a hybrid system which we analyze using the infinitesimal perturbation analysis (IPA) to obtain a complete online solution through an event-driven gradient-based algorithm which is also robust with respect to the uncertainty model used. The resulting controller depends on observing the events required to excite the gradient-based algorithm, which cannot be guaranteed. We solve this problem by introducing a new metric for the objective function which creates a potential field guaranteeing that gradient values are nonzero. This approach is compared to an alternative graph-based target-visit scheduling and dwell times optimization algorithm. The simulation examples are included to demonstrate the proposed methods.