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.

翻译：协作式悬吊空中运输系统极易受到空气动力学效应与推力不确定性等多源扰动的影响。为实现精确负载操控，现有方法通常依赖额外传感器测量缆绳方向或负载位姿，这增加了系统成本与复杂性。一个根本性问题依然存在：仅利用无人机里程计信息，在多源扰动下负载位姿是否可观测？为回答此问题，本研究聚焦于双无人机-杆系统，利用可观测性秩判据证明当仅存在两种或更少类型的集总扰动时，整个系统是可观测的。据我们所知，我们首次提出此类结论，该结果为通过最小化传感器配置实现更具成本效益与鲁棒性的系统铺平了道路。随后，为验证此分析，我们考虑扰动仅作用于无人机的情形，并开发了一种复合扰动滤波方案。设计了一种基于扰动观测器的误差状态扩展卡尔曼滤波器，用于状态与扰动估计，该滤波器提升了在流形 $(\\mathbb{R}^3)^2\\times(TS^2)^3$上演化的整个系统的估计性能。我们的仿真与实验测试验证了仅利用无人机里程计信息完全估计系统状态与扰动是可行的。