A technique that allows a formation-enforcing control (FEC) derived from graph rigidity theory to interface with a realistic relative localization system is proposed in this paper. Recent research in sensor-based multi-robot control has given rise to multiple modalities of mutual relative localization systems. In particular, vision-based relative localization has reached the stage where it can be carried onboard lightweight UAVs in order to retrieve the relative positions and relative orientations of cooperating units. A separate stream of development spawned distributed formation-enforcing control which can lead individual robots into a desired formation using relative localization of their neighbors. These two fields naturally complement each other by achieving real-world flights of UAVs in formation without the need for absolute localization in the world. However, real relative localization systems are, without exception, burdened by non-negligible sensory noise, which is typically not fully taken into account in formation-enforcing control algorithms. Such noise can lead to rapid changes in velocity, which further interferes with visual localization. Our approach provides a solution to these challenges, enabling practical deployment of FEC under realistic conditions, as we demonstrated in real-world experiments.

翻译：本文提出了一种技术，使基于图刚性理论的编队保持控制（FEC）能够与实际的相对定位系统相衔接。近年来，基于传感器的多机器人控制研究催生了多种相互相对定位模式。特别是，基于视觉的相对定位已达到可搭载于轻型无人机上，以获取合作单元的相对位置和相对定向的阶段。另一条独立的研究方向催生了分布式编队保持控制，该控制利用邻居的相对定位引导个体机器人进入期望编队。这两个领域通过实现无人机在不依赖全局绝对定位的情况下进行实际编队飞行而自然互补。然而，实际相对定位系统无一例外地受不可忽略的传感噪声困扰，而编队保持控制算法通常未充分考量这一因素。此类噪声会导致速度急剧变化，进而干扰视觉定位。我们的方法为这些挑战提供了解决方案，使FEC能在实际条件下实用化部署，这已通过真实世界实验得到验证。