Observability Analysis and Composite Disturbance Filtering for a Bar Tethered to Dual UAVs Subject to Multi-source Disturbances

TL;DR

This paper proves the payload's pose is observable with only drone odometry under certain disturbances, and develops a disturbance filtering scheme validated by simulations and experiments for cost-effective aerial transport.

Contribution

It demonstrates payload pose observability with minimal sensors and introduces a novel disturbance filtering method for a two-drone-bar system under multi-source disturbances.

Findings

Payload pose is observable with limited sensors under specific disturbance conditions.

The proposed disturbance observer improves state and disturbance estimation accuracy.

Simulation and experimental results confirm the effectiveness of the filtering scheme.

Abstract

Cooperative suspended aerial transportation is highly susceptible to multi-source disturbances such as aerodynamic effects and thrust uncertainties. To achieve precise load manipulation, existing methods often rely on extra sensors to measure cable directions or the payload's pose, which increases the system cost and complexity. A fundamental question remains: is the payload's pose observable under multi-source disturbances using only the drones' odometry information? To answer this question, this work focuses on the two-drone-bar system and proves that the whole system is observable when only two or fewer types of lumped disturbances exist by using the observability rank criterion. To the best of our knowledge, we are the first to present such a conclusion and this result paves the way for more cost-effective and robust systems by minimizing their sensor suites. Next, to validate this analysis, we consider the situation where the disturbances are only exerted on the drones, and develop a composite disturbance filtering scheme. A disturbance observer-based error-state extended Kalman filter is designed for both state and disturbance estimation, which renders improved estimation performance for the whole system evolving on the manifold $(\mathbb{R}^3)^2\times(TS^2)^3$. Our simulation and experimental tests have validated that it is possible to fully estimate the state and disturbance of the system with only odometry information of the drones.