去中心化且完全自主：基于距离的协作定位与导航在微航空器上的应用
Decentralized and Fully Onboard: Range-Aided Cooperative Localization and Navigation on Micro Aerial Vehicles
AI总结：
本文提出了一种去中心化的基于距离的协作定位与导航方法，通过块坐标下降和因子图推断实现编队控制，适用于微航空器在复杂环境中的自主导航。
摘要：
协调控制一组机器人具有挑战性，因为集中式方法（所有计算都在中央机器上进行）扩展性差，而全局参考的外部定位系统可能并不总是可用。在本工作中，我们考虑了基于距离的去中心化定位和编队控制问题。在这种情况下，每个机器人通过结合仅来自机载里程计传感器的数据和与其他机器人之间的距离测量来估计其相对姿态。此外，每个机器人计算必要的控制输入，以协作导航环境以完成特定任务，例如在特定编队中移动的同时监控区域。我们提出了一种块坐标下降方法用于定位，该方法不需要机器人之间的严格协调。我们提出了一种新的编队控制方法，作为因子图上的推断，考虑了状态估计的不确定性，并且可以高效求解。我们的基于距离的定位和基于编队的导航方法是完全去中心化的，不需要专门的轨迹来维持编队，并实现了分米级的定位和编队控制精度。我们通过多个实际实验展示了我们的方法，涉及在多样化的室内和室外环境中进行编队飞行。
英文摘要：
Controlling a team of robots in a coordinated manner is challenging because centralized approaches (where all computation is performed on a central machine) scale poorly, and globally referenced external localization systems may not always be available. In this work, we consider the problem of range-aided decentralized localization and formation control. In such a setting, each robot estimates its relative pose by combining data only from onboard odometry sensors and distance measurements to other robots in the team. Additionally, each robot calculates the control inputs necessary to collaboratively navigate an environment to accomplish a specific task, for example, moving in a desired formation while monitoring an area. We present a block coordinate descent approach to localization that does not require strict coordination between the robots. We present a novel formulation for formation control as inference on factor graphs that takes into account the state estimation uncertainty and can be solved efficiently. Our approach to range-aided localization and formation-based navigation is completely decentralized, does not require specialized trajectories to maintain formation, and achieves decimeter-level positioning and formation control accuracy. We demonstrate our approach through multiple real experiments involving formation flights in diverse indoor and outdoor environments.
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。
