Nonlinear Model Predictive Control for Cooperative Transportation and Manipulation of Cable Suspended Payloads with Multiple Quadrotors
Nonlinear Model Predictive Control for Cooperative Transportation and Manipulation of Cable Suspended Payloads with Multiple Quadrotors
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DOI：
10.1109/iros55552.2023.10341785
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Guanrui Li;Giuseppe Loianno
中科院分区：
文献类型：
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作者：
Guanrui Li;Giuseppe Loianno
配备有操纵机构的四旋翼等自主微型飞行器（MAV）具有协助人类执行诸如建造和包裹递送等任务的潜力。由于其重量轻、成本低和设计简单，缆索是操纵机构的一种有前途的选择。然而，设计控制和规划策略的电缆机构提出了挑战，由于间接的负载驱动，非线性配置空间，高度耦合的系统动力学。在本文中，我们提出了一种新的非线性模型预测控制（NMPC）方法，使一组四旋翼操纵刚体有效载荷在所有6个自由度通过悬挂电缆。作为滚动优化的一部分，我们的方法可以同时利用可用的机械系统冗余来执行其他任务，例如机器人间分离和避障，同时尊重有效载荷动态和致动器约束。为了解决实时计算的要求和可扩展性，我们采用了一个轻量级的状态向量参数化，只包括有效载荷状态的所有六个自由度。这也使得能够在SE（3）歧管负载配置空间上规划轨迹，从而也降低了规划复杂性。我们通过仿真和真实世界的实验验证所提出的方法。
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.