Closed-Loop Model Identification and MPC-based Navigation of Quadcopters: A Case Study of Parrot Bebop 2

TL;DR

This paper develops a simplified linear model and a steady-state-aware MPC for quadcopters, enabling real-time, constraint-satisfying navigation on limited onboard computational resources, demonstrated on Parrot Bebop 2.

Contribution

It introduces a linear modeling approach and a low-complexity MPC tailored for quadcopters, validated through experiments on Parrot Bebop 2.

Findings

The linear model effectively captures quadcopter dynamics with reduced complexity.

The steady-state-aware MPC guarantees constraint satisfaction in real-time.

Experimental results confirm the approach's practicality on Parrot Bebop 2.

Abstract

The growing potential of quadcopters in various domains, such as aerial photography, search and rescue, and infrastructure inspection, underscores the need for real-time control under strict safety and operational constraints. This challenge is compounded by the inherent nonlinear dynamics of quadcopters and the on-board computational limitations they face. This paper aims at addressing these challenges. First, this paper presents a comprehensive procedure for deriving a linear yet efficient model to describe the dynamics of quadrotors, thereby reducing complexity without compromising efficiency. Then, this paper develops a steady-state-aware Model Predictive Control (MPC) to effectively navigate quadcopters, while guaranteeing constraint satisfaction at all times. The main advantage of the steady-state-aware MPC is its low computational complexity, which makes it an appropriate choice for systems with limited computing capacity, like quadcopters. This paper considers Parrot Bebop 2 as the running example, and experimentally validates and evaluates the proposed algorithms.