RVC-NMPC：基于互斥碰撞回避的非线性模型预测控制
RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight
AI总结：
本文提出一种基于RVC-NMPC的非线性模型预测控制方法，通过高效计算和直接整合互斥速度约束，实现高频率的碰撞回避控制，适用于敏捷无人机飞行。
摘要：
本文提出了一种基于非线性模型预测控制（NMPC）的互碰撞回避方法，结合时间相关的互斥速度约束（RVCs）。与大多数现有方法不同，所提出的方法仅依赖于其他机器人可观察的信息，消除了对过度通信需求的必要性。计算高效的RVC计算算法，以及这些约束直接整合到NMPC问题公式化中的控制器层面，使整个流程能够以100Hz的速度运行。这种高处理速度，结合对受控无人员飞行器（UAVs）的非线性动态建模，是使所提出方法适用于敏捷UAV飞行的关键特征。所提出的方法通过广泛模拟和现实世界实验进行了评估，模拟了现实条件下的场景，包括多达10个UAV和高达25m/s的速度，以及现实世界实验中高达30m/s²的加速度。与最先进方法的比较表明，在具有挑战性的场景中飞行时间减少了31%，同时在所有试验中保持无碰撞导航。
英文摘要：
This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velocity Constraints (RVCs). Unlike most existing methods, the proposed approach relies solely on observable information about other robots, eliminating the need for excessive communication. The computationally efficient algorithm for computing RVCs, together with the direct integration of these constraints into the NMPC problem formulation at the controller level, allows the whole pipeline to run at 100 Hz. This high processing rate, combined with modeled nonlinear dynamics of the controlled Uncrewed Aerial Vehicles (UAVs), is a key feature that facilitates the use of the proposed approach for agile UAV flight. The proposed approach was evaluated through extensive simulations emulating real-world conditions in scenarios involving up to 10 UAVs and velocities of up to 25 m/s, and in real-world experiments with accelerations up to 30 m/s$^2$. Comparison with the state of the art shows 31% improvement in terms of flight time reduction in challenging scenarios, while maintaining a collision-free navigation in all trials.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
