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

TL;DR

The paper addresses the challenge of estimating a bar-shaped payload pose suspended by two UAVs under multi-source disturbances using only the drones' odometry. It first proves, via the observability rank criterion, that payload pose is observable when at most two disturbance types act, establishing a theoretical basis for sensorless estimation. Building on this, it develops a disturbance observer-based error-state EKF (DO-ESEKF) operating on manifolds to jointly estimate payload state and lumped disturbances. The approach is validated through comprehensive simulations and indoor experiments, showing robust, accurate payload pose estimation far from equilibrium and outperforming linearized observers, thereby enabling cost-effective, sensor-light payload manipulation in practice.

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.

Figures (13)

• Figure 1: Schematic of the two-drone-bar system
• Figure 2: DO-based ESEKF workflow
• Figure 3: The estimation results of payload position in the simulation of point stabilization
• Figure 4: The estimation results of disturbances in the simulation of point stabilization
• Figure 5: The comparison of payload estimation with the linearized method xu2024oscillation in the simulation of point stabilization

Theorems & Definitions (4)

• Remark 1
• Remark 2
• Remark 3
• Remark 4