MRS UAV系统：以自主无人机推动可复现研究、实际部署与教育的前沿
The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles
AI总结：
本文提出一种多旋翼无人机控制与估计系统，采用多框架定位和基于旋转矩阵的表示，支持GNSS拒止环境下的复杂任务，并通过开源实现和竞赛验证其可复现性与实际部署价值。
摘要：
我们提出了一种多旋翼无人机控制与估计系统，旨在通过逼真的仿真和实际实验支持可复现的研究。我们提出了一种独特的多框架定位范式，利用多个传感器同时在不同参考框架下估计无人机的状态。该系统能够在GNSS和GNSS拒止环境中执行复杂任务，包括室外-室内过渡，以及执行冗余估计器以备份不可靠的定位源。我们设计了两种反馈控制方案：一种用于精确和激进机动，另一种用于在噪声状态估计下实现稳定平滑飞行。所提出的控制与估计流程在构建时不使用三维方向中的欧拉/泰特-布莱恩角表示。相反，我们依赖旋转矩阵和一种新颖的基于航向的约定来表示标准多旋翼直升机在三维空间中一个自由旋转自由度。我们提供了一个积极维护且文档完善的开源实现，包括无人机、传感器和定位系统的逼真仿真。所提出的系统是多年在多机器人系统、空中集群、空中操纵、运动规划和遥感领域应用研究的成果。我们所有结果都得到了实际系统部署的支持，这些部署将系统塑造成目前呈现的形式。此外，该系统在我们布拉格捷克理工大学团队参加著名的MBZIRC 2017和2020机器人竞赛以及DARPA SubT挑战赛期间得到了使用。每次，我们的团队都能在全球最佳竞争者中占据领先位置。每一次挑战都激励团队改进系统，并在紧迫的截止日期内获得大量高质量经验。
英文摘要：
We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.
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。
