Many approaches to multi-robot coordination are susceptible to failure due to communication loss and uncertainty in estimation. We present a real-time communication-free distributed navigation algorithm certified by control barrier functions, that models and controls the onboard sensing behavior to keep neighbors in the limited field of view for position estimation. The approach is robust to temporary tracking loss and directly synthesizes control to stabilize visual contact through control Lyapunov-barrier functions. The main contributions of this paper are a continuous-time robust trajectory generation and control method certified by control barrier functions for distributed multi-robot systems and a discrete optimization procedure, namely, MPC-CBF, to approximate the certified controller. In addition, we propose a linear surrogate of high-order control barrier function constraints and use sequential quadratic programming to solve MPC-CBF efficiently.

翻译：许多多机器人协同方法因通信中断与估计不确定性而易失效。本文提出一种基于控制屏障函数认证的实时无通信分布式导航算法，该算法通过建模与控制机载感知行为，使相邻机器人始终保持在有限视场内以实现位置估计。该方法对临时跟踪丢失具有鲁棒性，并直接通过控制李雅普诺夫-屏障函数综合控制以稳定视觉接触。本文的主要贡献包括：针对分布式多机器人系统提出一种基于控制屏障函数认证的连续时间鲁棒轨迹生成与控制方法，以及一种离散优化流程（即MPC-CBF）以近似该认证控制器。此外，我们提出高阶控制屏障函数约束的线性代理模型，并采用序列二次规划高效求解MPC-CBF。