多旋翼协同吊运负载，用事件触发的非线性模型预测控制提升效率。
Event-Triggered Nonlinear Model Predictive Control for Cooperative Cable-Suspended Payload Transportation with Multi-Quadrotors
- 基于事件触发机制的分布式非线性模型预测控制
- 实现在六自由度下的实时轨迹规划与资源优化
- 适合需要高效协同运输的无人机系统应用
自主微型飞行器（MAVs），尤其是四旋翼，已在建筑和快递等任务中展现巨大潜力。利用缆绳进行操作具有轻量化、低成本和结构简单的优势。然而，设计有效的控制与规划策略面临诸多挑战：负载间接驱动、非线性配置空间及高度耦合的动力学特性。本文提出一种新型事件触发式分布式非线性模型预测控制（NMPC）方法，专用于多四旋翼协同吊运负载的任务。该方法有效解决负载操控、机器人间距保持、避障和轨迹跟踪问题，同时优化计算与通信资源使用。通过引入事件触发机制，减少不必要的计算与通信，提升能效并延长飞行器续航。所提方法采用轻量级状态向量参数化，聚焦于负载在全部六个自由度的状态，实现对SE(3)流形上的高效轨迹规划，显著降低规划复杂度并保障实时可行性。通过大量仿真验证，该方法在动态且资源受限环境中表现出色。
原文摘要 · Abstract (English)
Autonomous Micro Aerial Vehicles (MAVs), particularly quadrotors, have shown significant potential in assisting humans with tasks such as construction and package delivery. These applications benefit greatly from the use of cables for manipulation mechanisms due to their lightweight, low-cost, and simple design. However, designing effective control and planning strategies for cable-suspended systems presents several challenges, including indirect load actuation, nonlinear configuration space, and highly coupled system dynamics. In this paper, we introduce a novel event-triggered distributed Nonlinear Model Predictive Control (NMPC) method specifically designed for cooperative transportation involving multiple quadrotors manipulating a cable-suspended payload. This approach addresses key challenges such as payload manipulation, inter-robot separation, obstacle avoidance, and trajectory tracking, all while optimizing the use of computational and communication resources. By integrating an event-triggered mechanism, our NMPC method reduces unnecessary computations and communication, enhancing energy efficiency and extending the operational range of MAVs. The proposed method employs a lightweight state vector parametrization that focuses on payload states in all six degrees of freedom, enabling efficient planning of trajectories on the SE(3) manifold. This not only reduces planning complexity but also ensures real-time computational feasibility. Our approach is validated through extensive simulation, demonstrating its efficacy in dynamic and resource-constrained environments.
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