A Control Barrier Function Candidate for Quadrotors with Limited Field of View

TL;DR

This paper introduces a novel Control Barrier Function approach that enhances robustness in vision-based control for quadrotors with limited field of view, addressing sensor limitations without requiring distance measurements.

Contribution

It proposes a new CBF-based method that removes dependence on unknown measurement errors, providing robustness guarantees for agents with complex dynamics.

Findings

Robust control barrier function guarantees against bounded measurement errors.

Successful numerical simulations with a quadrotor tracking a trajectory.

Demonstrated effectiveness in maintaining camera field of view during navigation.

Abstract

The problem of control based on vision measurements (bearings) has been amply studied in the literature; however, the problem of addressing the limits of the field of view of physical sensors has received relatively less attention (especially for agents with non-trivial dynamics). The technical challenge is that, as in most vision-based control approaches, a standard approach to the problem requires knowing the distance between cameras and observed features in the scene, which is not directly available. Instead, we present a solution based on a Control Barrier Function (CBF) approach that uses a splitting of the original differential constraint to effectively remove the dependence on the unknown measurement error. Compared to the current literature, our approach gives strong robustness guarantees against bounded distance estimation errors. We showcase the proposed solution with the numerical simulations of a double integrator and a quadrotor tracking a trajectory while keeping the corners of a rectangular gate in the camera field of view.