This paper introduces a novel methodology for the cooperative control of multiple quadrotors transporting cablesuspended payloads, emphasizing obstacle-aware planning and event-based Nonlinear Model Predictive Control (NMPC). Our approach integrates trajectory planning with real-time control through a combination of the A* algorithm for global path planning and NMPC for local control, enhancing trajectory adaptability and obstacle avoidance. We propose an advanced event-triggered control system that updates based on events identified through dynamically generated environmental maps. These maps are constructed using a dual-camera setup, which includes multi-camera systems for static obstacle detection and event cameras for high-resolution, low-latency detection of dynamic obstacles. This design is crucial for addressing fast-moving and transient obstacles that conventional cameras may overlook, particularly in environments with rapid motion and variable lighting conditions. When new obstacles are detected, the A* algorithm recalculates waypoints based on the updated map, ensuring safe and efficient navigation. This real-time obstacle detection and map updating integration allows the system to adaptively respond to environmental changes, markedly improving safety and navigation efficiency. The system employs SLAM and object detection techniques utilizing data from multi-cameras, event cameras, and IMUs for accurate localization and comprehensive environmental mapping. The NMPC framework adeptly manages the complex dynamics of multiple quadrotors and suspended payloads, incorporating safety constraints to maintain dynamic feasibility and stability. Extensive simulations validate the proposed approach, demonstrating significant enhancements in energy efficiency, computational resource management, and responsiveness.

翻译：本文提出了一种用于多四旋翼无人机协同运输缆索悬挂载荷的新方法，重点在于障碍物感知规划与基于事件触发的非线性模型预测控制。该方法通过结合用于全局路径规划的A*算法与用于局部控制的NMPC，将轨迹规划与实时控制相集成，从而增强了轨迹适应性与避障能力。我们提出了一种先进的基于事件触发的控制系统，其更新依据是通过动态生成的环境地图所识别的事件。这些地图由双摄像头系统构建，该系统包括用于静态障碍物检测的多摄像头系统，以及用于高分辨率、低延迟动态障碍物检测的事件相机。该设计对于应对传统相机可能忽略的快速移动和瞬态障碍物至关重要，尤其是在具有快速运动和变化光照条件的环境中。当检测到新障碍物时，A*算法会根据更新后的地图重新计算路径点，确保安全高效的导航。这种实时障碍物检测与地图更新集成使系统能够自适应地响应环境变化，显著提高了安全性和导航效率。该系统采用SLAM和目标检测技术，利用来自多摄像头、事件相机和IMU的数据进行精确定位和全面的环境建图。NMPC框架巧妙地管理多四旋翼无人机及悬挂载荷的复杂动力学，并纳入安全约束以保持动态可行性和稳定性。大量仿真验证了所提方法的有效性，证明了其在能源效率、计算资源管理和系统响应性方面的显著提升。