用预测机制提升多机器人避障安全，避免死锁和迂回。
A Predictive Cooperative Collision Avoidance for Multi-Robot Systems Using Control Barrier Function
- 构建预测安全矩阵，基于最小特征值判断未来安全
- 引入预判项后对测量误差鲁棒，且无振荡现象
- 通过最小姿态角计算逃生速度，避免死锁与明显停滞
基于控制屏障函数（CBF）的方法能在二次规划框架下以最小修改量保证安全，适用于高安全要求系统。然而，多数CBF衍生方法仅关注当前时刻的安全性，缺乏对未来时间窗口的前瞻推理。本文提出一种预测安全矩阵，并基于其最小特征值构建安全约束。在轨迹跟踪模块中嵌入预设解冲突路径策略，通过计算最小姿态角的逃逸速度来处理死锁问题。对比实验表明，引入预测项后系统对测量不确定性具有鲁棒性，且不产生振荡。所提方法有效避免了大幅绕行，未出现明显停滞。
原文摘要 · Abstract (English)
Control barrier function (CBF)-based methods provide the minimum modification necessary to formally guarantee safety in the context of quadratic programming, and strict safety guarantee for safety critical systems. However, most CBF-related derivatives myopically focus on present safety at each time step, a reasoning over a look-ahead horizon is exactly missing. In this paper, a predictive safety matrix is constructed. We then consolidate the safety condition based on the smallest eigenvalue of the proposed safety matrix. A predefined deconfliction strategy of motion paths is embedded into the trajectory tracking module to manage deadlock conflicts, which computes the deadlock escape velocity with the minimum attitude angle. Comparison results show that the introduction of the predictive term is robust for measurement uncertainty and is immune to oscillations. The proposed deadlock avoidance method avoids a large detour, without obvious stagnation.
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