A Hybrid Modelling Approach for Aerial Manipulators

TL;DR

This paper introduces a hybrid modeling approach for aerial manipulators that combines quaternion and Lagrangian methods to accurately and efficiently model their complex, non-linear dynamics, validated through experiments and applied in control.

Contribution

It presents a novel hybrid dynamics modeling method for aerial manipulators that merges quaternion and Lagrangian frameworks, reducing singularities and complexity.

Findings

Model validated against real-time physics engine

Effective in real-time control applications

Handles complex, non-linear dynamics accurately

Abstract

Aerial manipulators (AM) exhibit particularly challenging, non-linear dynamics; the UAV and the manipulator it is carrying form a tightly coupled dynamic system, mutually impacting each other. The mathematical model describing these dynamics forms the core of many solutions in non-linear control and deep reinforcement learning. Traditionally, the formulation of the dynamics involves Euler angle parametrization in the Lagrangian framework or quaternion parametrization in the Newton-Euler framework. The former has the disadvantage of giving birth to singularities and the latter of being algorithmically complex. This work presents a hybrid solution, combining the benefits of both, namely a quaternion approach leveraging the Lagrangian framework, connecting the singularity-free parameterization with the algorithmic simplicity of the Lagrangian approach. We do so by offering detailed insights into the kinematic modeling process and the formulation of the dynamics of a general aerial manipulator. The obtained dynamics model is validated experimentally against a real-time physics engine. A practical application of the obtained dynamics model is shown in the context of a computed torque feedback controller (feedback linearization), where we analyze its real-time capability with increasingly complex models.