Problem
Research questions and friction points this paper is trying to address.
vision-based formation control
input saturations
underwater robots
Vision-based Underwater Formation Control With Input Saturations via Barrier Lyapunov Functions
🤖 AI Summary
本文提出一种基于视觉的水下机器人编队控制框架，利用障碍Lyapunov函数处理感知和避碰约束，并通过二次规划考虑执行器限制。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
vision-based formation control
barrier Lyapunov functions
input saturations
command-filtered backstepping
adaptive relaxation
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