Free-Space Ellipsoid Graphs for Multi-Agent Target Monitoring

TL;DR

This paper introduces a novel ellipsoid-based decomposition framework for free space in environments, enabling efficient multi-agent target monitoring through improved planning, coordination, and collision avoidance in complex 2D and 3D spaces.

Contribution

It presents a new method to decompose free space into ellipsoids for better reasoning and control in multi-agent persistent monitoring tasks, integrating high-level planning with collision-free control.

Findings

Effective in multi-agent tracking of dynamic targets

Works in obstacle-rich 2D and 3D environments

Demonstrates improved coordination and safety

Abstract

We apply a novel framework for decomposing and reasoning about free space in an environment to a multi-agent persistent monitoring problem. Our decomposition method represents free space as a collection of ellipsoids associated with a weighted connectivity graph. The same ellipsoids used for reasoning about connectivity and distance during high level planning can be used as state constraints in a Model Predictive Control algorithm to enforce collision-free motion. This structure allows for streamlined implementation in distributed multi-agent tasks in 2D and 3D environments. We illustrate its effectiveness for a team of tracking agents tasked with monitoring a group of target agents. Our algorithm uses the ellipsoid decomposition as a primitive for the coordination, path planning, and control of the tracking agents. Simulations with four tracking agents monitoring fifteen dynamic targets in obstacle-rich environments demonstrate the performance of our algorithm.