Event-driven Trajectory Optimization for Data Harvesting in Multi-Agent Systems

TL;DR

This paper introduces an event-driven online trajectory optimization method for multi-agent data harvesting, addressing delays and stochastic data generation with scalable, robust solutions using IPA and trajectory parameterization.

Contribution

It presents a novel event-driven approach with trajectory parameterization and IPA optimization, improving scalability and robustness in multi-agent data harvesting tasks.

Findings

The method effectively minimizes data collection and delivery delays.

It demonstrates robustness to stochastic data generation processes.

Comparisons show advantages over existing graph-based algorithms.

Abstract

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.