Collision-Free Trajectory Design for 2D Persistent Monitoring Using Second-Order Agents

TL;DR

This paper develops a collision-free trajectory design method for second-order agents in 2D persistent monitoring, incorporating an active sensing model and optimizing trajectories via IPA and gradient descent.

Contribution

It introduces a novel active sensing model considering velocity, and derives a parametric trajectory optimization approach ensuring collision avoidance.

Findings

Effective collision-free trajectories achieved in simulations

Method is computationally efficient compared to traditional approaches

Trajectories can be tailored to different 2D curve families

Abstract

This paper considers a two-dimensional persistent monitoring problem by controlling movements of second-order agents to minimize some uncertainty metric associated with targets in a dynamic environment. In contrast to common sensing models depending only on the distance from a target, we introduce an active sensing model which considers the distance between an agent and a target, as well as the agent's velocity. We propose an objective function which can achieve a collision-free agent trajectory by penalizing all possible collisions. Applying structural properties of the optimal control derived from the Hamiltonian analysis, we limit agent trajectories to a simpler parametric form under a family of 2D curves depending on the problem setting, e.g. ellipses and Fourier trajectories. Our collision-free trajectories are optimized through an event-driven Infinitesimal Perturbation Analysis (IPA) and gradient descent method. Although the solution is generally locally optimal, this method is computationally efficient and offers an alternative to other traditional time-driven methods. Finally, simulation examples are provided to demonstrate our proposed results.