Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks

TL;DR

This paper introduces a distributed Gaussian process-based control law enabling drone networks with cameras to collaboratively pursue a moving target, with proven bounded errors and validated through simulations and real-world experiments.

Contribution

It presents a novel distributed Gaussian process approach for cooperative visual pursuit control in drone networks, handling unknown target motion.

Findings

Estimation and control errors are ultimately bounded.

The method is effective in simulations and real drone experiments.

Abstract

In this paper, we propose a control law for camera-equipped drone networks to pursue a target rigid body with unknown motion based on distributed Gaussian process. First, we consider the situation where each drone has its own dataset, and learned the unknown target motion in a distributed manner. Second, we propose a control law using the distributed Gaussian processes, and show that the estimation and control errors are ultimately bounded. Furthermore, the effectiveness of the proposed method is verified by using simulations and experiments with actual drones.