多旋翼协同吊运负载,无需中心控制,自适应估重,定位精准。
Decentralized Geometric Control for Cable-Suspended Payload Transport with Adaptive Mass Estimation

- 每架无人机通过本地缆绳测量自估受力,实现无中心协调的合力平衡。
- 仿真中负载追踪均方根误差33.8厘米,计算开销低,抗风能力强。
- 适合需要安全协同运输的无人机系统,如应急救援或大型物资吊运。
协同空中运输需控制器兼顾非线性流形几何、无中心协调及操作安全约束。本文提出GPAC——一种四层分层架构,使N架四旋翼无人机在无中央协调、不交换缆绳状态或自适应参数的前提下运输悬吊负载。核心思想是隐式协调:每架无人机基于本地缆绳测量独立估计自身有效载荷份额,合力自动收敛至正确总量,即使未知N值或负载质量;负载位置由各机自身缆绳几何局部重建,仅需低频邻近位置广播用于避碰。GPAC直接作用于完整非线性配置流形,集成几何位置与姿态控制、防摆调节、抗风扩展状态观测器、无需持续激励的并发学习质量估计算法,以及优先级排序的控制屏障函数(CBF)启发的安全滤波器,具备输入到状态安全(ISSf)裕度,在单一约束激活下精确成立。兼容性结果表明,滤波器的力修正保持期望姿态在SO(3)姿态控制器的几乎全局稳定区域。高保真仿真包含柔性缆绳、机载传感器融合与风扰动——所有控制与估计回路闭环通过估计算法——在13次种子测试中,负载追踪均方根误差为33.8厘米(变异系数2.8%),每架无人机计算成本极低。
原文摘要 · Abstract (English)
Cooperative aerial transport requires controllers that respect nonlinear manifold geometry, operate without centralized coordination, and respect operational safety constraints. To address these demands, we present GPAC, a four-layer hierarchical architecture that enables $N$ quadrotors to transport a cable-suspended payload without a central coordinator or by exchanging cable states or adaptive parameters. The key insight is implicit coordination: each quadrotor independently estimates its effective load share from local cable measurements, so combined forces converge to the correct total, even without knowledge of $N$ or the payload mass; the payload position is reconstructed locally from each agent's own cable geometry, and the only inter-agent communication is a low-rate neighbor-position broadcast for collision avoidance. GPAC operates directly on the full nonlinear configuration manifold and integrates geometric position and attitude control, anti-swing regulation, an extended-state observer for wind rejection, concurrent learning-based mass estimation without persistent excitation, and a priority-ordered control barrier function (CBF)-inspired safety filter that reduces operational risk, with input-to-state safety (ISSf) margins that hold exactly under single-constraint activation. A compatibility result shows that the filter's force modifications keep the desired attitude within the almost-global stability region of the $\mathrm{SO}(3)$ attitude controller. Finally, high-fidelity simulation with flexible cables, onboard sensor fusion, and wind turbulence -- with all control and estimation loops closed through the estimator -- yields a mean payload-tracking RMSE of 33.8 cm (2.8\% coefficient of variation over 13 seeds) at a low per-agent computational cost.
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