Cooperative control for multi-agent persistent monitoring problems

In persistent monitoring tasks, cooperating mobile agents are used to monitor a dynamically changing environment that cannot be fully covered by a stationary team of agents. The exploration process leads to the discovery of various "points of interest" (targets) to be perpetually monitored. Through an optimal control approach, the first part of this dissertation shows that in a one-dimensional mission space the solution can be reduced to a simpler parametric problem. The behavior of agents under optimal control is described by a hybrid system which can be analyzed using Infinitesimal Perturbation Analysis (IPA) to obtain an on-line solution. IPA allows the modeling of virtually arbitrary stochastic effects in target uncertainty and its event-driven nature renders the solution scalable in the number of events rather than the state space. The second part of this work extends the results of the one-dimensional persistent monitoring problem to a two-dimensional space with constrained agent mobility. Under a general graph setting, the properties of the one-dimensional optimal control solution are largely inherited. The solution involves the design of agent trajectories defined by both the sequence of nodes to be visited and the amount of time spent at each node. A class of distributed threshold-based parametric controllers is proposed to reduce the computational complexity. These parameters are optimized through an event-driven IPA gradient-based algorithm and yield optimal controllers within this family of threshold-based policies. The performance of the threshold-based parametric controller is close to that of the optimal controller derived through dynamic programming and its computational complexity is smaller by orders of magnitude. Although effective, the aforementioned optimal controls are established on the assumption that agents are all connected via a centralized controller which is energy-consuming and unreliable in adversarial environments. The third part of this work extends the previous controls by developing decentralized controllers which distribute functionality to the agents so that each one acts upon local information and sparse communication with neighbors. The complexity of decentralization for persistent monitoring problems is significant given agent mobility and the overall time-varying graph topology. Conditions are identified and a decentralized framework is proposed under which the centralized solution can be exactly recovered in a decentralized event-driven manner based on local information -- except for one event requiring communication from a non-neighbor agent