  We propose a new event-driven method for on-line trajectory optimization to
solve the data harvesting problem: in a two-dimensional mission space, N mobile
agents are tasked with the collection of data generated at M stationary sources
and delivery to a base with the goal of minimizing expected collection and
delivery delays. We define a new performance measure that addresses the event
excitation problem in event-driven controllers and formulate an optimal control
problem. The solution of this problem provides some insights on its structure,
but it is computationally intractable, especially in the case where the data
generating processes are stochastic. We propose an agent trajectory
parameterization in terms of general function families which can be
subsequently optimized on line through the use of Infinitesimal Perturbation
Analysis (IPA). Properties of the solutions are identified, including
robustness with respect to the stochastic data generation process and
scalability in the size of the event set characterizing the underlying hybrid
dynamical system. Explicit results are provided for the case of elliptical and
Fourier series trajectories and comparisons with a state-of-the-art graph-based
algorithm are given.
