融合速度障碍与控制屏障函数，实现更安全平滑的多智能体避障。
Multi-Agent Obstacle Avoidance using Velocity Obstacles and Control Barrier Functions
- 用速度障碍引导路径，控制屏障函数确保安全约束
- 在二阶积分器和汽车模型下，成功率与路径平滑性均优于基线
- 适合需要高安全性和流畅运动的自动驾驶等场景
速度障碍（VO）方法为动态障碍物与智能体间的避障策略提供了一种范式。尽管在简单多智能体环境中表现良好，但传统VO方法无法保证安全性，且在常见情况下常表现出过度保守的行为。本文提出将VO策略用于路径引导，同时结合控制屏障函数（CBF）保障安全，克服了VO的过度保守问题，并实现了形式化安全保证。我们在基于二阶积分器和汽车模型的基准对比实验中验证了该方法，结果表明，相较于基线方法，该方案在路径平滑性、避障能力及任务成功率方面均有显著提升。
原文摘要 · Abstract (English)
Velocity Obstacles (VO) methods form a paradigm for collision avoidance strategies among moving obstacles and agents. While VO methods perform well in simple multi-agent environments, they don't guarantee safety and can show overly conservative behavior in common situations. In this paper, we propose to combine a VO-strategy for guidance with a CBF-approach for safety, which overcomes the overly conservative behavior of VOs and formally guarantees safety. We validate our method in a baseline comparison study, using 2nd order integrator and car-like dynamics. Results support that our method outperforms the baselines w.r.t. path smoothness, collision avoidance, and success rates.
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