用视觉约束实现多智能体安全跟随，确保始终看得见领头者。
Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods
- 将相机视野限制作为状态约束，结合控制屏障函数保证安全
- 通过神经网络与双框检测实时估计状态，适应不同环境
- 在仿真中验证了跨环境鲁棒性，适合无人机协同任务
本文研究了一类分布式领头-跟随编队控制问题，每个智能体使用固定于自身的摄像头进行状态估计，但其视场角（FOV）有限。主要挑战在于需协调智能体运动与其摄像头视角，以确保持续观测到领头者，从而实现准确可靠的状态估计。为此，提出一种新型感知感知意识的分布式安全控制方案，将视场角限制作为状态约束，并采用基于控制屏障函数（CBF）的二次规划方法，确保由这些约束定义的安全集具有前向不变性。此外，开发了基于神经网络和双边界框的估计算法，结合时间滤波器，直接从实时图像数据中估计系统状态，在多种环境下均表现出一致性能。在 Gazebo 模拟器中的对比实验表明，所提框架在两种不同环境中均具备有效性和鲁棒性。
原文摘要 · Abstract (English)
This paper addresses a distributed leader-follower formation control problem for a group of agents, each using a body-fixed camera with a limited field of view (FOV) for state estimation. The main challenge arises from the need to coordinate the agents' movements with their cameras' FOV to maintain visibility of the leader for accurate and reliable state estimation. To address this challenge, we propose a novel perception-aware distributed leader-follower safe control scheme that incorporates FOV limits as state constraints. A Control Barrier Function (CBF) based quadratic program is employed to ensure the forward invariance of a safety set defined by these constraints. Furthermore, new neural network based and double bounding boxes based estimators, combined with temporal filters, are developed to estimate system states directly from real-time image data, providing consistent performance across various environments. Comparison results in the Gazebo simulator demonstrate the effectiveness and robustness of the proposed framework in two distinct environments.
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