无人机群在无通信下仍能稳定保持视觉追踪，防丢失。
Robust Trajectory Generation and Control for Quadrotor Motion Planning with Field-of-View Control Barrier Certification
- 用控制屏障函数保证视觉视野内追踪，无需通信
- 临时失联后可自动恢复视线，控制稳定
- 适合无人机编队、机器人协同等场景
许多多机器人协同方法因通信中断和估计不确定性而失效。本文提出一种基于控制屏障函数的实时、无通信分布式导航算法，通过建模和控制机载感知行为，确保邻近机器人始终处于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，并通过控制李雅普诺夫-屏障函数直接合成控制律以稳定视觉接触。主要贡献包括：一种连续时间的鲁棒轨迹生成与控制方法，由控制屏障函数认证适用于分布式多机器人系统；以及一种名为MPC-CBF的离散优化过程，用于近似该认证控制器。此外，提出高阶控制屏障函数的线性代理模型，并采用序列二次规划高效求解MPC-CBF。
原文摘要 · Abstract (English)
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.
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