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.

翻译：控制机器人团队以协调方式行动具有挑战性，因为集中式方法（所有计算均在中央机器上执行）可扩展性差，且全局参考的外部定位系统可能并非始终可用。在本研究中，我们探讨了基于距离辅助的去中心化定位与编队控制问题。在此设定下，每个机器人仅通过融合机载里程计传感器数据及与团队中其他机器人的距离测量值，来估计其相对位姿。此外，每个机器人会计算必要的控制输入，以协作导航环境并完成特定任务，例如在监测区域时保持期望编队行进。我们提出了一种无需机器人间严格协调的块坐标下降定位方法。我们提出了一种新颖的编队控制公式，将其建模为因子图上的推理问题，该公式考虑了状态估计的不确定性并能高效求解。我们提出的基于距离辅助的定位与编队导航方法完全去中心化，无需为维持编队执行特定轨迹，并实现了分米级的定位与编队控制精度。我们通过涉及多样室内外环境编队飞行的多次真实实验验证了所提方法。