基于障碍李雅普诺夫函数的输入饱和下视觉水下编队控制
Vision-based Underwater Formation Control With Input Saturations via Barrier Lyapunov Functions
AI总结：
本文提出一种无通信的视觉编队控制框架，利用重新中心化障碍李雅普诺夫函数处理感知与避碰约束，结合命令滤波反步和二次规划应对输入饱和，并在Gazebo中通过SITL仿真验证。
AI中文摘要：
本文提出了一种无通信框架，用于在感知约束、避碰要求和输入饱和条件下，对全驱动水下机器人进行基于视觉的编队控制。重新中心化的障碍李雅普诺夫函数编码了感知和避碰约束，而命令滤波反步法将设计扩展到二阶车辆动力学。所得到的控制目标通过一个显式考虑执行器限制的二次规划来实现。保守的感知域提供了与物理限制的裕度，并在必要时自适应地放宽，允许暂时违反保守界限。所提出的方法通过Gazebo中的真实软件在环（SITL）仿真得到验证。
英文摘要：
In this work, we propose a communication-free framework for vision-based formation control of fully actuated underwater robots subject to sensing constraints, collision-avoidance requirements, and input saturations. Recentered barrier Lyapunov functions encode sensing and collision-avoidance constraints, while command-filtered backstepping extends the design to the second-order vehicle dynamics. The resulting control objective is enforced through a quadratic program that explicitly accounts for actuator limits. Conservative sensing domains provide margins from the physical limits and are adaptively relaxed when necessary, allowing temporary violation of the conservative bounds. The proposed approach is validated through realistic Software-in-the-Loop (SITL) simulations in Gazebo.
