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

2025-12-10

AI Summary

Existing cooperative transportation methods for dual UAVs suspending a rigid rod rely on additional attitude or cable sensors to mitigate multi-source disturbances—such as aerodynamic effects and thrust uncertainties—increasing system cost and complexity. Method: This paper establishes, for the first time, a theoretical proof of full state observability—including load position, orientation, and disturbances—on the manifold $(mathbb{R}^3)^2 imes (TS^2)^3$, using only onboard UAV odometry under the condition that disturbance types are limited to at most two. Building upon this, we propose a nonlinear estimation framework integrating a disturbance observer (DOB) with an error-state extended Kalman filter (ES-EKF). Results: Simulation and experimental results demonstrate high-precision joint estimation without auxiliary sensors; disturbance and attitude estimation errors are reduced by over 40% compared to conventional approaches, significantly enhancing the feasibility of lightweight, robust control.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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 $(mathbb{R}^3)^2 imes(TS^2)^3$. 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.

Problem

• Observability of payload pose under multi-source disturbances using drone odometry only
• Composite disturbance filtering for state and disturbance estimation without extra sensors
• Validation of system observability and estimation performance via simulation and experiments

Innovation

• Observability analysis using drones' odometry only
• Composite disturbance filtering with error-state Kalman filter
• State and disturbance estimation on manifold without extra sensors