This article studies the problem of distributed formation control for multiple robots by using onboard ultra wide band (UWB) ranging and inertial odometer (IO) measurements. Although this problem has been widely studied, a fundamental limitation of most works is that they require each robot's pose and sensor measurements are expressed in a common reference frame. However, it is inapplicable for nonholonomic robot formations due to the practical difficulty of aligning IO measurements of individual robot in a common frame. To address this problem, firstly, a concurrent-learning based estimator is firstly proposed to achieve relative localization between neighboring robots in a local frame. Different from most relative localization methods in a global frame, both relative position and orientation in a local frame are estimated with only UWB ranging and IO measurements. Secondly, to deal with information loss caused by directed communication topology, a cooperative localization algorithm is introduced to estimate the relative pose to the leader robot. Thirdly, based on the theoretical results on relative pose estimation, a distributed formation tracking controller is proposed for nonholonomic robots. Both gazebo physical simulation and real-world experiments conducted on networked TurtleBot3 nonholonomic robots are provided to demonstrate the effectiveness of the proposed method.

翻译：本文研究利用车载超宽带测距与惯性里程计测量实现多机器人分布式编队控制的问题。尽管该问题已被广泛研究，但大多数工作的一个根本局限在于要求每个机器人的位姿与传感器测量均表达于一个公共参考系中。然而，由于在实际中难以将各机器人的惯性里程计测量对齐至公共坐标系，该方法对于非完整机器人编队并不适用。为解决此问题，首先提出一种基于并发学习的估计器，以实现相邻机器人在局部坐标系中的相对定位。与多数在全局坐标系中进行相对定位的方法不同，本方法仅利用超宽带测距与惯性里程计测量即可估计局部坐标系中的相对位置与朝向。其次，为应对有向通信拓扑导致的信息丢失问题，引入一种协同定位算法以估计相对于领航机器人的相对位姿。再次，基于相对位姿估计的理论结果，提出一种适用于非完整机器人的分布式编队跟踪控制器。通过在网络化TurtleBot3非完整机器人平台上进行的Gazebo物理仿真与实物实验，验证了所提方法的有效性。