Abstract
This paper addresses the problem of tracking the position of a cable-suspended payload carried by an unmanned aerial vehicle, with a focus on real-world deployment and minimal hardware requirements. In contrast to many existing approaches that rely on motion-capture systems, additional onboard cameras, or instrumented payloads, we propose a framework that uses only standard onboard sensors--specifically, real-time kinematic global navigation satellite system measurements and data from the onboard inertial measurement unit--to estimate and control the payload's position. The system models the full coupled dynamics of the aerial vehicle and payload, and integrates a linear Kalman filter for state estimation, a model predictive contouring control planner, and an incremental model predictive controller. The control architecture is designed to remain effective despite sensing limitations and estimation uncertainty. Extensive simulations demonstrate that the proposed system achieves performance comparable to control based on ground-truth measurements, with only minor degradation (< 6%). The system also shows strong robustness to variations in payload parameters. Field experiments further validate the framework, confirming its practical applicability and reliable performance in outdoor environments using only off-the-shelf aerial vehicle hardware.
Abstract (translated)
本文研究了由无人驾驶航空器携带的悬挂在缆索上的负载的位置跟踪问题，特别关注实际部署和最小硬件需求。与许多现有的依赖运动捕捉系统、额外机载摄像机或仪器化负载的方法不同，我们提出了一种仅使用标准机载传感器——实时动态全球导航卫星系统测量和机载惯性测量单元数据——来估计和控制负载位置的框架。该系统模拟了空中飞行器和负载之间的完整耦合动力学，并整合了一个线性卡尔曼滤波器用于状态估计、模型预测轮廓控制器规划器以及增量模型预测控制器。该控制系统架构即使在传感限制和估计不确定性的情况下也能保持有效性。广泛的仿真结果表明，所提出的系统实现了与基于地面真实测量控制相媲美的性能，仅略有下降（<6%）。此外，该系统还显示出对负载参数变化的强鲁棒性。现场实验进一步验证了该框架的有效性，在仅使用现成的飞行器硬件的情况下确认其在户外环境中的实际应用性和可靠性能。
URL
https://arxiv.org/abs/2508.11547
