Event-driven Trajectory Optimization for Data Harvesting in Multi-Agent Systems
For multi-agent systems tasked with data collection from stationary sources, this work provides an online trajectory optimization method that is robust to stochastic data generation and scalable, though it is an incremental improvement over existing methods.
This paper addresses the data harvesting problem in multi-agent systems, aiming to minimize data collection and delivery delays. The proposed event-driven trajectory optimization method, using Infinitesimal Perturbation Analysis, achieves robustness and scalability, with elliptical and Fourier series trajectories outperforming a state-of-the-art graph-based algorithm.
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.