Autonomous Micro Aerial Vehicles (MAVs) such as quadrotors equipped with manipulation mechanisms have the potential to assist humans in tasks such as construction and package delivery. Cables are a promising option for manipulation mechanisms due to their low weight, low cost, and simple design. However, designing control and planning strategies for cable mechanisms presents challenges due to indirect load actuation, nonlinear configuration space, and highly coupled system dynamics. In this paper, we propose a novel Nonlinear Model Predictive Control (NMPC) method that enables a team of quadrotors to manipulate a rigid-body payload in all 6 degrees of freedom via suspended cables. Our approach can concurrently exploit, as part of the receding horizon optimization, the available mechanical system redundancies to perform additional tasks such as inter-robot separation and obstacle avoidance while respecting payload dynamics and actuator constraints. To address real-time computational requirements and scalability, we employ a lightweight state vector parametrization that includes only payload states in all six degrees of freedom. This also enables the planning of trajectories on the $SE(3)$ manifold load configuration space, thereby also reducing planning complexity. We validate the proposed approach through simulation and real-world experiments.

翻译：自主微小型飞行器（如配备操作机构的多旋翼无人机）具有协助人类完成建筑、包裹递送等任务的潜力。缆绳因其重量轻、成本低、设计简单而成为操作机构的有前景选择。然而，由于间接负载驱动、非线性构型空间及高度耦合的系统动态特性，缆绳机构的控制与规划策略设计面临挑战。本文提出一种新型非线性模型预测控制（NMPC）方法，使多旋翼无人机编队能够通过悬挂缆绳在全部6个自由度上操控刚体负载。该方法可在滚动时域优化中同步利用机械系统的冗余特性，在满足负载动力学与执行器约束的同时执行附加任务（如机器人间避碰与避障）。为满足实时计算需求与可扩展性，我们采用轻量化状态向量参数化方法，仅包含负载的六个自由度状态参数。这也使得轨迹规划可在$SE(3)$流形负载构型空间中进行，从而降低规划复杂度。通过仿真与真实世界实验验证了所提方法的有效性。